8bit conversion

ABSTRACT

Described herein is a method for beam profile analysis using at least one camera. The method includes:a) at least one data acquisition step;b) at least one image compression step; andc) at least one evaluation step.

TECHNICAL FIELD

The invention relates to a method for beam profile analysis and adetector for determining a position of at least one object and a mobiledevice. The invention further relates to various uses of the detector.The device, method and uses specifically may be employed for example invarious areas of daily life, gaming, traffic technology, productiontechnology, security technology, photography such as digital photographyor video photography for arts, documentation or technical purposes,medical technology or in the sciences. Further, the device and methodspecifically may be used for scanning one or more objects and/or forscanning a scenery, such as for generating a depth profile of an objector of a scenery, e.g. in the field of architecture, metrology,archaeology, arts, medicine, engineering or manufacturing. However,other applications are also possible.

BACKGROUND ART

3D imaging and/or material detection using beam profile analysis (BPA),also denoted as depth-from-photon-ratio technique (DPR) are describede.g. in WO 2018/091649 A1, WO 2018/091638 A1, WO 2018/091640 A1 and C.Lennartz, F. Schick, S. Metz, “Whitepaper—Beam Profile Analysis for 3Dimaging and material detection” Apr. 28, 2021, Ludwigshafen, Germany,the full content of which is included by reference. Processing device,imager, and the like used for evaluation often require 8 bit data volumesuch that an image signal is compressed from 10 bit to 8 bit.

However, known compression methods yield to loss in signal quality,resolution, contrast and dynamic range. In addition, for beam profileanalysis it would be desirable to preserve imaged image data as good aspossible in order to ensure reliable measurement results.

Compression of image data is known for other specific applications e.g.from EP 3 820 150 A1, EP 3 185 555 A1, or Wolfgang Förstner, “ImagePreprocessing for Feature Extraction in Digital Intensity, Color andRange Images”, Proceedings of the International Summer School on DataAnalysis and the Statistical Foundations of Geomatics Chania, Crete,Greece, May 25-30, 1998, in Springer Lecture Notes on Earth Sciences.However, for 3D imaging and/or material detection using beam profileanalysis suitable compression of image data is still an unsolvedproblem.

Problem to be Solved

It is therefore an object of the present invention to provide a methodfor beam profile analysis and a detector facing the above-mentionedtechnical challenges of known devices and methods. Specifically, it isan object of the present invention to provide devices and methods whichallow for reliable 3D imaging and/or material detection using beamprofile analysis with low requirements in terms of technical resources,time and cost.

SUMMARY

This problem is addressed by a method for beam profile analysis and adetector with the features of the independent claims. Advantageousembodiments which might be realized in an isolated fashion or in anyarbitrary combinations are listed in the dependent claims as well asthroughout the specification.

In a first aspect of the present invention, a method for beam profileanalysis using at least one camera is disclosed.

The camera has at least one sensor element having a matrix of opticalsensors. The optical sensors each having a light-sensitive area. Eachoptical sensor is designed to generate at least one sensor signal inresponse to an illumination of its respective light-sensitive area by areflection light beam propagating from the object to the camera.

The method steps may be performed in the given order or may be performedin a different order. Further, one or more additional method steps maybe present which are not listed. Further, one, more than one or even allof the method steps may be performed repeatedly.

The method comprises the following steps:

-   -   a) at least one data acquisition step, wherein the data        acquisition step comprises illuminating at least one object with        at least one illumination pattern comprising at least one        illumination feature by using at least one projector, wherein        the projector comprises at least one emitter configured for        generating at least one light beam, wherein the data acquisition        step further comprises imaging, by using the camera, at least        one reflection image comprising at least one reflection feature        generated by the object in response to illumination by the        illumination feature, wherein the reflection feature comprises        at least one beam profile, wherein the reflection image has a        first bit depth;    -   b) at least one image compression step, wherein the image        compression step comprises compressing the reflection image into        a compressed reflection image having a second bit depth lower        than the first bit depth by using at least one evaluation        device, wherein the compression comprises applying a non-linear        grey value transformation on the sensor signals;    -   c) at least one evaluation step, wherein the evaluation step        comprises evaluating the compressed reflection image by using        the evaluation device, wherein the evaluation comprises        determining at least one longitudinal coordinate for the        reflection feature by analysis of its respective beam profile.

The method may be computer-implemented. The term “computer-implemented”as used herein is a broad term and is to be given its ordinary andcustomary meaning to a person of ordinary skill in the art and is not tobe limited to a special or customized meaning. The term specifically mayrefer, without limitation, to the fact that the method involving atleast one computer and/or at least one computer network. The computerand/or computer network may comprise at least one processor which isconfigured for performing at least one of the method steps of the methodaccording to the present invention. Specifically, each of the methodsteps is performed by the computer and/or computer network. The methodmay be performed completely automatically, specifically without userinteraction.

The term “beam profile analysis”, also denoted asdepth-from-photon-ratio (DPR), as used herein is a broad term and is tobe given its ordinary and customary meaning to a person of ordinaryskill in the art and is not to be limited to a special or customizedmeaning. The term specifically may refer, without limitation, to amethod for determining at least one physical quantity or property of anobject such as a longitudinal coordinate and/or material property byusing the beam profile of a reflection light beam originating from theobject. With respect to beam profile analysis reference is made to WO2018/091649 A1, WO 2018/091638 A1, WO 2018/091640 A1 and C. Lennartz, F.Schick, S. Metz, “Whitepaper—Beam Profile Analysis for 3D imaging andmaterial detection” Apr. 28, 2021, Ludwigshafen, Germany, the fullcontent of which is included by reference. Beam profile analysis may beperformed by illuminating the object to be measured by the projectoremitting a, e.g. dot, illumination pattern. The reflection of each lightspot, i.e. a reflection feature, may be captured by the camera and itsbeam profile is then analyzed. From its specific beam profile,information about the longitudinal coordinate and/or material of themeasured object can be determined.

The term “ray” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the artand is not to be limited to a special or customized meaning. The termspecifically may refer, without limitation, to a line that isperpendicular to wavefronts of light which points in a direction ofenergy flow. The term “beam” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art and is not to be limited to a special or customized meaning.The term specifically may refer, without limitation, to a collection ofrays. In the following, the terms “ray” and “beam” will be used assynonyms. The term “light beam” as used herein is a broad term and is tobe given its ordinary and customary meaning to a person of ordinaryskill in the art and is not to be limited to a special or customizedmeaning. The term specifically may refer, without limitation, to anamount of light, specifically an amount of light traveling essentiallyin the same direction, including the possibility of the light beamhaving a spreading angle or widening angle. The term “beam profile” sused herein is a broad term and is to be given its ordinary andcustomary meaning to a person of ordinary skill in the art and is not tobe limited to a special or customized meaning. The term specifically mayrefer, without limitation, to a spatial distribution, in particular inat least one plane perpendicular to the propagation of the light beam,of an intensity of the light beam. The beam profile may be a transverseintensity profile of the light beam. The beam profile may be a crosssection of the light beam. The beam profile may be selected from thegroup consisting of a trapezoid beam profile; a triangle beam profile; aconical beam profile and a linear combination of Gaussian beam profiles.Other embodiments are feasible, however. The beam profile may also bedenoted as reflection profile or spot profile.

The term “object” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the artand is not to be limited to a special or customized meaning. The termspecifically may refer, without limitation, to an arbitrary object to bemeasured, in particular a surface or region, which is configured toreflect at least partially at least one light beam impinging on theobject. For example, the object may be a human being, in particularskin. The object may comprise a surface or region, which is configuredfor at least partially one or more of reflecting and/or scatteringand/or emitting in response to at least one light beam impinging on theobject. The light beam may originate from the projector illuminating theobject, wherein the light beam is reflected and/or scattered by theobject. The object may be part of a scene comprising the object andfurther surrounding environment.

The projector, the camera and the evaluation device may be elements of adetector. The term “detector” as used herein is a broad term and is tobe given its ordinary and customary meaning to a person of ordinaryskill in the art and is not to be limited to a special or customizedmeaning. The term specifically may refer, without limitation, to anarbitrary sensor device configured for determining and/or detectingand/or sensing the object. The detector may be a stationary device or amobile device. Further, the detector may be a stand-alone device or mayform part of another device, such as a computer, a vehicle or any otherdevice. Further, the detector may be a hand-held device. Otherembodiments of the detector are feasible. The detector may be one ofattached to or integrated into a mobile device such as a mobile phone orsmartphone. The detector may be integrated in a mobile device, e.g.within a housing of the mobile device. Additionally or alternatively,the detector, or at least one component of the detector, may be attachedto the mobile device such as by using a connector such as a USB orphone-connector such as the headphone jack.

The data acquisition step comprises illuminating at least one objectwith at least one illumination pattern comprising at least oneillumination feature by using at least one projector. The projectorcomprises at least one emitter configured for generating at least onelight beam.

The term “projector”, also denoted as light projector, as used herein isa broad term and is to be given its ordinary and customary meaning to aperson of ordinary skill in the art and is not to be limited to aspecial or customized meaning. The term specifically may refer, withoutlimitation, to an optical device configured to project at least oneillumination pattern onto the object, specifically onto a surface of theobject. The projector is configured for illuminating the object with atleast one illumination pattern comprising a plurality of illuminationfeatures.

The term “pattern” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the artand is not to be limited to a special or customized meaning. The termspecifically may refer, without limitation, to an arbitrary known orpre-determined arrangement comprising a plurality of arbitrarily shapedfeatures such as symbols. The pattern may comprise a plurality offeatures. The pattern may comprise an arrangement of periodic ornon-periodic features. The term “at least one illumination pattern” asused herein is a broad term and is to be given its ordinary andcustomary meaning to a person of ordinary skill in the art and is not tobe limited to a special or customized meaning. The term specifically mayrefer, without limitation, to at least one arbitrary pattern comprisingthe illumination features adapted to illuminate at least one part of theobject.

The term “illumination feature” as used herein is a broad term and is tobe given its ordinary and customary meaning to a person of ordinaryskill in the art and is not to be limited to a special or customizedmeaning. The term specifically may refer, without limitation, to atleast one at least partially extended feature of the pattern. Theillumination pattern comprises a plurality of illumination features. Forexample, the illumination pattern comprises at least 10, 20, 100, 1000or even more illumination features.

The illumination pattern may comprise at least one pattern selected fromthe group consisting of: at least one quasi random pattern; at least oneSobol pattern; at least one quasiperiodic pattern; at least one pointpattern, in particular a pseudo-random point pattern; at least one linepattern; at least one stripe pattern; at least one checkerboard pattern;at least one triangular pattern; at least one rectangular pattern; atleast one hexagonal pattern or a pattern comprising further convextilings. The illumination pattern may exhibit the at least oneillumination feature selected from the group consisting of: at least onepoint; at least one line; at least two lines such as parallel orcrossing lines; at least one point and one line; at least onearrangement of periodic features; at least one arbitrary shaped featuredpattern. For example, the illumination pattern comprises at least onepattern comprising at least one pre-known feature. For example, theillumination pattern comprises at least one line pattern comprising atleast one line. For example, the illumination pattern comprises at leastone line pattern comprising at least two lines such as parallel orcrossing lines. For example, the projector may be configured forgenerate and/or to project a cloud of points or non-point-like features.For example, the projector may be configured for generate a cloud ofpoints or non-point-like features such that the illumination pattern maycomprise a plurality of point features or non-point-like features. Asfurther used herein, the term “illuminating the object with at least oneillumination pattern” may refer to providing the at least oneillumination pattern for illuminating the at least one object.

The projector comprises at least one emitter configured for generatingat least one light beam. The projector may comprise at least one arrayof emitters. Each of the emitters may be configured for emitting atleast one light beam.

The light beam generated by the emitter generally may propagate parallelto an optical axis or tilted with respect to the optical axis, e.g.including an angle with the optical axis. The projector may beconfigured such that the light beam propagates from the projectortowards the object along an optical axis of the detector. For thispurpose, the detector may comprise at least one reflective element,preferably at least one prism, for deflecting the light beams onto theoptical axis. As an example, the light beams and the optical axis mayinclude an angle of less than 10°, preferably less than 5° or even lessthan 2°. Other embodiments, however, are feasible. Further, the lightbeams may be on the optical axis or off the optical axis. As an example,the light beam or light beams may be parallel to the optical axis havinga distance of less than 10 mm to the optical axis, preferably less than5 mm to the optical axis or even less than 1 mm to the optical axis ormay even coincide with the optical axis.

The term “emitter” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the artand is not to be limited to a special or customized meaning. The termspecifically may refer, without limitation, to at least one arbitrarydevice configured for providing the at least one light beam forillumination of the object. Each of the emitters may be and/or maycomprise at least one element selected from the group consisting of atleast one laser source such as at least one semi-conductor laser, atleast one double heterostructure laser, at least one external cavitylaser, at least one separate confinement heterostructure laser, at leastone quantum cascade laser, at least one distributed Bragg reflectorlaser, at least one polariton laser, at least one hybrid silicon laser,at least one extended cavity diode laser, at least one quantum dotlaser, at least one volume Bragg grating laser, at least one IndiumArsenide laser, at least one Gallium Arsenide laser, at least onetransistor laser, at least one diode pumped laser, at least onedistributed feedback lasers, at least one quantum well laser, at leastone interband cascade laser, at least one semiconductor ring laser, atleast one vertical cavity surface-emitting laser (VCSEL); at least onenon-laser light source such as at least one LED or at least one lightbulb.

The array of emitters may be a two-dimensional or one dimensional array.The array may comprise a plurality of emitters arranged in a matrix. Theterm “matrix” as used herein is a broad term and is to be given itsordinary and customary meaning to a person of ordinary skill in the artand is not to be limited to a special or customized meaning. The termspecifically may refer, without limitation, to an arrangement of aplurality of elements in a predetermined geometrical order. The matrixspecifically may be or may comprise a rectangular matrix having one ormore rows and one or more columns. The rows and columns specifically maybe arranged in a rectangular fashion. However, other arrangements arefeasible, such as nonrectangular arrangements. As an example, circulararrangements are also feasible, wherein the elements are arranged inconcentric circles or ellipses about a center point.

For example, the emitters may be an array of VCSELs. The term“vertical-cavity surface-emitting laser” as used herein is a broad termand is to be given its ordinary and customary meaning to a person ofordinary skill in the art and is not to be limited to a special orcustomized meaning. The term specifically may refer, without limitation,to a semiconductor laser diode configured for laser beam emissionperpendicular with respect to a top surface. Examples for VCSELs can befound e.g. inen.wikipedia.org/wiki/Vertical-cavity_surface-emitting_laser. VCSELs aregenerally known to the skilled person such as from WO 2017/222618 A.Each of the VCSELs is configured for generating at least one light beam.The VCSELs may be arranged on a common substrate or on differentsubstrates. The array may comprise up to 2500 VCSELs. For example, thearray may comprise 38×25 VCSELs, such as a high power array with 3.5 W.For example, the array may comprise 10×27 VCSELs with 2.5 W. Forexample, the array may comprise 96 VCSELs with 0.9 W. A size of thearray, e.g. of 2500 elements, may be up to 2 mm×2 mm.

The light beam emitted by the respective emitter may have a wavelengthof 300 to 1100 nm, preferably 500 to 1100 nm. For example, the lightbeam may have a wavelength of 940 nm. For example, light in the infraredspectral range may be used, such as in the range of 780 nm to 3.0 μm.Specifically, the light in the part of the near infrared region wheresilicon photodiodes are applicable specifically in the range of 700 nmto 1100 nm may be used. The emitter may be configured for generating theat least one illumination pattern in the infrared region, in particularin the near infrared region. Using light in the near infrared region mayallow that light is not or only weakly detected by human eyes and isstill detectable by silicon sensors, in particular standard siliconsensors. For example, the emitters may be an array of VCSELs. The VCSELsmay be configured for emitting light beams at a wavelength range from800 to 1000 nm. For example, the VCSELs may be configured for emittinglight beams at 808 nm, 850 nm, 940 nm, or 980 nm. Preferably the VCSELsemit light at 940 nm, since terrestrial sun radiation has a localminimum in irradiance at this wavelength, e.g. as described in CIE085-1989 “Solar spectral Irradiance”.

The projector may comprise at least one transfer device configured forgenerating the illumination features from the light beams impinging onthe transfer device. The term “transfer device”, also denoted as“transfer system”, may generally refer to one or more optical elementswhich are adapted to modify the light beam, such as by modifying one ormore of a beam parameter of the light beam, a width of the light beam ora direction of the light beam. The transfer device may comprise at leastone imaging optical device. The transfer device specifically maycomprise one or more of: at least one lens, for example at least onelens selected from the group consisting of at least one focus-tunablelens, at least one aspheric lens, at least one spheric lens, at leastone Fresnel lens; at least one diffractive optical element; at least oneconcave mirror; at least one beam deflection element, preferably atleast one mirror; at least one beam splitting element, preferably atleast one of a beam splitting cube or a beam splitting mirror; at leastone multi-lens system; at least one holographic optical element; atleast one meta optical element. Specifically, the transfer devicecomprises at least one refractive optical lens stack. Thus, the transferdevice may comprise a multi-lens system having refractive properties.

The projector may comprise at least one diffractive optical element(DOE) configured for generating the illumination pattern. The DOE may beconfigured for generating multiple light beams from a single incominglight beam.

The data acquisition step further comprises imaging, by using thecamera, at least one reflection image comprising at least one reflectionfeature generated by the object in response to illumination by theillumination feature. The term “camera” as used herein is a broad termand is to be given its ordinary and customary meaning to a person ofordinary skill in the art and is not to be limited to a special orcustomized meaning. The term specifically may refer, without limitation,to a device having at least one imaging element configured for recordingor capturing spatially resolved one-dimensional, two-dimensional or eventhree-dimensional optical data or information. The camera may be adigital camera. As an example, the camera may comprise at least onecamera chip, such as at least one CCD chip and/or at least one CMOS chipconfigured for recording images. The camera may be or may comprise atleast one near infrared camera.

As used herein, without limitation, the term “image” specifically mayrelate to data recorded by using a camera, such as a plurality ofelectronic readings from the camera, such as the pixels of the camerachip. The image itself may comprise pixels, the pixels of the imagecorrelating to pixels of the matrix of the sensor element. Consequently,when referring to “pixels”, reference is either made to the units ofimage information generated by the single pixels of the sensor elementor to the single pixels of the sensor element directly. The image may beat least one two-dimensional image. As used herein, the term “twodimensional image” may generally refer to an image having informationabout transversal coordinates such as the dimensions of height andwidth. The image may be an RGB (red green blue) image. However, otherembodiments are feasible.

The camera, besides the at least one camera chip or imaging chip, maycomprise further elements, such as one or more optical elements, e.g.one or more lenses. As an example, the camera may be a fix-focus camera,having at least one lens which is fixedly adjusted with respect to thecamera. Alternatively, however, the camera may also comprise one or morevariable lenses which may be adjusted, automatically or manually.

The camera may be a camera of a mobile device such as of notebookcomputers, tablets or, specifically, cell phones such as smart phonesand the like. Thus, specifically, the camera may be part of a mobiledevice which, besides the camera, comprises one or more data processingdevices such as one or more data processors. Other cameras, however, arefeasible.

The term “mobile device” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art and is not to be limited to a special or customized meaning.The term specifically may refer, without limitation, to a mobileelectronics device, more specifically to a mobile communication devicesuch as a cell phone or smart phone. Additionally or alternatively, themobile device may also refer to a tablet computer or another type ofportable computer.

The term “sensor element” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art and is not to be limited to a special or customized meaning.The term specifically may refer, without limitation, to a device or acombination of a plurality of devices configured for sensing at leastone parameter. In the present case, the parameter specifically may be anoptical parameter, and the sensor element specifically may be an opticalsensor element. The sensor element may be formed as a unitary, singledevice or as a combination of several devices. The matrix specificallymay be or may comprise a rectangular matrix having one or more rows andone or more columns. The rows and columns specifically may be arrangedin a rectangular fashion. However, other arrangements are feasible, suchas nonrectangular arrangements. As an example, circular arrangements arealso feasible, wherein the elements are arranged in concentric circlesor ellipses about a center point. For example, the matrix may be asingle row of pixels. Other arrangements are feasible.

The term “optical sensor” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art and is not to be limited to a special or customized meaning.The term specifically may refer, without limitation, to alight-sensitive device for detecting a light beam, such as for detectingan illumination and/or a light spot generated by at least one lightbeam. As further used herein, a “light-sensitive area” generally refersto an area of the optical sensor which may be illuminated externally, bythe at least one light beam, in response to which illumination the atleast one sensor signal is generated. The light-sensitive area mayspecifically be located on a surface of the respective optical sensor.Other embodiments, however, are feasible. The optical sensors of thematrix specifically may be equal in one or more of size, sensitivity andother optical, electrical and mechanical properties. The light-sensitiveareas of all optical sensors of the matrix specifically may be locatedin a common plane, the common plane preferably facing the object, suchthat a light beam propagating from the object to the camera may generatea light spot on the common plane.

As used herein, the term “the optical sensors each having at least onelight sensitive area” may refer to configurations with a plurality ofsingle optical sensors each having one light sensitive area and toconfigurations with one combined optical sensor having a plurality oflight sensitive areas. Thus, the term “optical sensor” furthermorerefers to a light-sensitive device configured to generate one outputsignal, whereas, herein, a light-sensitive device configured to generatetwo or more output signals, for example at least one CCD and/or CMOSdevice, is referred to as two or more optical sensors. Each opticalsensor may be embodied such that precisely one light-sensitive area ispresent in the respective optical sensor, such as by providing preciselyone light-sensitive area which may be illuminated, in response to whichillumination precisely one uniform sensor signal is created for thewhole optical sensor. Thus, each optical sensor may be a single areaoptical sensor. The use of the single area optical sensors, however,renders the setup of the detector specifically simple and efficient.Thus, as an example, commercially available photosensors, such ascommercially available silicon photodiodes, each having precisely onesensitive area, may be used in the setup. Other embodiments, however,are feasible. Thus, as an example, an optical device comprising two,three, four or more than four light-sensitive areas may be used which isregarded as two, three, four or more than four optical sensors in thecontext of the present invention. As outlined above, the sensor elementcomprises a matrix of optical sensors. Thus, as an example, the opticalsensors may be part of or constitute a pixelated optical device. As anexample, the optical sensors may be part of or constitute at least oneCCD and/or CMOS device having a matrix of pixels, each pixel forming alight-sensitive area.

The optical sensors specifically may be or may comprise photodetectors,preferably inorganic photodetectors, more preferably inorganicsemiconductor photodetectors, most preferably silicon photodetectors.Specifically, the optical sensors may be sensitive in the infraredspectral range. All of the optical sensors of the matrix or at least agroup of the optical sensors of the matrix specifically may beidentical. Groups of identical optical sensors of the matrixspecifically may be provided for different spectral ranges, or alloptical sensors may be identical in terms of spectral sensitivity.Further, the optical sensors may be identical in size and/or with regardto their electronic or optoelectronic properties.

Specifically, the optical sensors may be or may comprise inorganicphotodiodes which are sensitive in the infrared spectral range,preferably in the range of 780 nm to 3.0 micrometers. Specifically, theoptical sensors may be sensitive in the part of the near infrared regionwhere silicon photodiodes are applicable specifically in the range of700 nm to 1000 nm. Infrared optical sensors which may be used foroptical sensors may be commercially available infrared optical sensors,such as infrared optical sensors commercially available under the brandname Hertzstueck™ from trinamiX GmbH, D-67056 Ludwigshafen am Rhein,Germany. Thus, as an example, the optical sensors may comprise at leastone optical sensor of an intrinsic photovoltaic type, more preferably atleast one semiconductor photodiode selected from the group consistingof: a Ge photodiode, an InGaAs photodiode, an extended InGaAsphotodiode, an InAs photodiode, an InSb photodiode, a HgCdTe photodiode.Additionally or alternatively, the optical sensors may comprise at leastone optical sensor of an extrinsic photovoltaic type, more preferably atleast one semiconductor photodiode selected from the group consistingof: a Ge:Au photodiode, a Ge:Hg photodiode, a Ge:Cu photodiode, a Ge:Znphotodiode, a Si:Ga photodiode, a Si:As photodiode. Additionally oralternatively, the optical sensors may comprise at least one bolometer,preferably a bolometer selected from the group consisting of a VObolometer and an amorphous Si bolometer.

The matrix may be composed of independent optical sensors. Thus, amatrix may be composed of inorganic photodiodes. Alternatively, however,a commercially available matrix may be used, such as one or more of aCCD detector, such as a CCD detector chip, and/or a CMOS detector, suchas a CMOS detector chip.

The optical sensors of the sensor element may form a sensor array or maybe part of a sensor array, such as the above-mentioned matrix. Thus, asan example, the sensor element may comprise an array of optical sensors,such as a rectangular array, having m rows and n columns, with m, n,independently, being positive integers. Preferably, more than one columnand more than one row is given, i.e. n>1, m>1. Thus, as an example, nmay be 2 to 16 or higher and m may be 2 to 16 or higher. Preferably, theratio of the number of rows and the number of columns is close to 1. Asan example, n and m may be selected such that 0.3≤m/n≤3, such as bychoosing m/n=1:1, 4:3, 16:9 or similar. As an example, the array may bea square array, having an equal number of rows and columns, such as bychoosing m=2, n=2 or m=3, n=3 or the like.

The matrix specifically may be a rectangular matrix having at least onerow, preferably a plurality of rows, and a plurality of columns. As anexample, the rows and columns may be oriented essentially perpendicular.As used herein, the term “essentially perpendicular” refers to thecondition of a perpendicular orientation, with a tolerance of e.g. ±20°or less, preferably a tolerance of ±10° or less, more preferably atolerance of ±5° or less. Similarly, the term “essentially parallel”refers to the condition of a parallel orientation, with a tolerance ofe.g. ±20° or less, preferably a tolerance of ±10° or less, morepreferably a tolerance of ±5° or less. In order to provide a wide rangeof view, the matrix specifically may have at least 10 rows, preferablyat least 50 rows, more preferably at least 100 rows. Similarly, thematrix may have at least 10 columns, preferably at least 50 columns,more preferably at least 100 columns. The matrix may comprise at least50 optical sensors, preferably at least 100 optical sensors, morepreferably at least 500 optical sensors. The matrix may comprise anumber of pixels in a multi-mega pixel range. Other embodiments,however, are feasible. Thus, in setups in which an axial rotationalsymmetry is to be expected, circular arrangements or concentricarrangements of the optical sensors of the matrix, which may also bereferred to as pixels, may be preferred.

The reflection light beam may propagate from the object towards thecamera. The reflection light beam may originate from the object. Theprojector may illuminate the object with the at least one illuminationpattern and the light is at least partially remitted, reflected and/orscattered by the object and, thereby, is at least partially directed as“reflection light beams” towards the camera.

The optical sensors may be sensitive in one or more of the ultraviolet,the visible or the infrared spectral range. Specifically, the opticalsensors may be sensitive in the visible spectral range from 500 nm to780 nm, most preferably at 650 nm to 750 nm or at 690 nm to 700 nm.Specifically, the optical sensors may be sensitive in the near infraredregion. Specifically, the optical sensors may be sensitive in the partof the near infrared region where silicon photodiodes are applicablespecifically in the range of 700 nm to 1000 nm. The optical sensors,specifically, may be sensitive in the infrared spectral range,specifically in the range of 780 nm to 3.0 micrometers. For example, theoptical sensors each, independently, may be or may comprise at least oneelement selected from the group consisting of a photodiode, a photocell,a photoconductor, a phototransistor or any combination thereof. Forexample, the optical sensors may be or may comprise at least one elementselected from the group consisting of a CCD sensor element, a CMOSsensor element, a photodiode, a photocell, a photoconductor, aphototransistor or any combination thereof. Any other type ofphotosensitive element may be used. The photosensitive element generallymay fully or partially be made of inorganic materials and/or may fullyor partially be made of organic materials. Most commonly, one or morephotodiodes may be used, such as commercially available photodiodes,e.g. inorganic semiconductor photodiodes.

The term “sensor signal” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art and is not to be limited to a special or customized meaning.The term specifically may refer, without limitation, to a signalgenerated by an optical sensor in response to the illumination by thelight beam. Specifically, the sensor signal may be or may comprise atleast one electrical signal, such as at least one analogue electricalsignal and/or at least one digital electrical signal. More specifically,the sensor signal may be or may comprise at least one voltage signaland/or at least one current signal. More specifically, the sensor signalmay comprise at least one photocurrent. Further, either raw sensorsignals may be used, or the image device, the optical sensor or anyother element may be configured for process or preprocess the sensorsignal, thereby generating secondary sensor signals, which may also beused as sensor signals, such as preprocessing by filtering or the like.

The light beam generated by the object, also denoted as reflection lightbeam, specifically may fully illuminate the sensor element such that thesensor element is fully located within the light beam with a width ofthe light beam being larger than the matrix. Contrarily, preferably, thereflection light beam specifically may create a light spot on the entirematrix which is smaller than the matrix, such that the light spot isfully located within the matrix. This situation may easily be adjustedby a person skilled in the art of optics by choosing one or moreappropriate lenses or elements having a focusing or defocusing effect onthe light beam, such as by using an appropriate transfer device.

The camera may comprise a transfer device configured for guiding thelight beam onto the optical sensors and for forming the reflection imageon the sensor element. The detector may comprise an optical axis. Forexample, the transfer device may constitute a coordinate system, whereina longitudinal coordinate z is a coordinate along an optical axis of thetransfer device. The coordinate system may be a polar coordinate systemin which the optical axis of the transfer device forms a z-axis and inwhich a distance from the z-axis and a polar angle may be used asadditional coordinates. For example, the transfer device may constitutea coordinate system in which the optical axis of the detector forms thez-axis and in which, additionally, an x-axis and a y-axis may beprovided which are perpendicular to the z-axis and which areperpendicular to each other. As an example, the detector may rest at aspecific point in this coordinate system, such as at the origin of thiscoordinate system. A direction parallel or antiparallel to the z-axismay be considered a longitudinal direction, and a coordinate along thez-axis may be considered a longitudinal coordinate. Any directionperpendicular to the z-axis may be considered a transversal direction,and the polar coordinate and/or the polar angle may be considered atransversal coordinate. Alternatively, other types of coordinate systemsmay be used. Thus, as an example, a polar coordinate system may be usedin which the optical axis forms a z-axis and in which a distance fromthe z-axis and a polar angle may be used as additional coordinates. Adirection parallel or antiparallel to the z-axis may be considered alongitudinal direction, and a coordinate along the z-axis may beconsidered a longitudinal coordinate. Any direction perpendicular to thez-axis may be considered a transversal direction, and the polarcoordinate and/or the polar angle may be considered a transversalcoordinate.

The camera may comprise a global shutter and/or may be operated in aglobal shutter modus. The term “global shutter” as used herein is abroad term and is to be given its ordinary and customary meaning to aperson of ordinary skill in the art and is not to be limited to aspecial or customized meaning. The term specifically may refer, withoutlimitation, to an electronic shutter configured for quantizing exposuretime of the sensor element. The global shutter may be configured suchthat exposure of each pixel of the sensor element starts and ends at thesame time.

The term “reflection image” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art and is not to be limited to a special or customized meaning.The term specifically may refer, without limitation, to an imagedetermined by the camera comprising a plurality of reflection features.The term “reflection feature” as used herein is a broad term and is tobe given its ordinary and customary meaning to a person of ordinaryskill in the art and is not to be limited to a special or customizedmeaning. The term specifically may refer, without limitation, to afeature in an image plane generated by the object in response toillumination with at least one illumination feature. The reflectionimage may comprise the at least one reflection pattern comprising thereflection features. The term “imaging at least one refection image” asused herein is a broad term and is to be given its ordinary andcustomary meaning to a person of ordinary skill in the art and is not tobe limited to a special or customized meaning. The term specifically mayrefer, without limitation, to one or more of capturing, recording andgenerating of the reflection image.

The reflection feature comprises at least one beam profile. Each of thereflection features comprises at least one beam profile. The term “beamprofile” as used herein is a broad term and is to be given its ordinaryand customary meaning to a person of ordinary skill in the art and isnot to be limited to a special or customized meaning. The termspecifically may refer, without limitation, to a spatial distribution,in particular in at least one plane perpendicular to the propagation ofthe light beam, of an intensity of the light beam. The beam profile maybe a transverse intensity profile of the light beam. The beam profilemay be a cross section of the light beam. The beam profile may beselected from the group consisting of a trapezoid beam profile; atriangle beam profile; a conical beam profile and a linear combinationof Gaussian beam profiles. Other embodiments are feasible, however.

The reflection image has a first bit depth. The term “bit depth”, alsodenoted as digitization depth, as used herein is a broad term and is tobe given its ordinary and customary meaning to a person of ordinaryskill in the art and is not to be limited to a special or customizedmeaning. The term specifically may refer, without limitation, to anumber of bits per pixel in the image. The higher the first bit depth,the more information can be stored. For example, the first bit depth isat least one bit depth selected from the range consisting of 9 to 16.For example, the first bit depth may be 10 bit. However, even higherfirst bit depths may be possible, e.g. 32 bit.

For example, for storage and/or transmission and/or further analysis thefirst bit depth may need to be compressed. The term “compression” asused herein is a broad term and is to be given its ordinary andcustomary meaning to a person of ordinary skill in the art and is not tobe limited to a special or customized meaning. The term specifically mayrefer, without limitation, to transforming information using fewer bitsthan the original representation. The compression may comprisequantization by compressing a range of values to a single quantum value.The quantization may be performed using a defined number of quantizationlevels.

As outlined above, the method comprises, in step b), at least one imagecompression step. The image compression step comprises compressing thereflection image into a compressed reflection image having a second bitdepth lower than the first bit depth by using at least one evaluationdevice. The second bit depth may be a number of bits per pixel of thecompressed reflection image. The second bit depth is lower than thefirst bit depth. For example, the second bit depth is 8 to 15 bit.

Usually, compressions are lossy, in particular may result into loss insignal range, resolution, and/or contrast. For reliable results usingbeam profile analysis, however, the signal may need to be as “physical”as possible. Further known techniques use equidistant quantization forimage sensors. However, in case of a linear camera having low darknoise, equidistant quantization may not be suitable. The camera may havelow dark noise. Therefore, a variance of a temporal noise may increaselinearly with the sensor signal. In order to be able to resolve a lownoise in a dark part of the picture, many quantization levels would berequired. As a result, however, in the bright part, the quantization maybecome much too fine, so that the standard deviation of the temporalnoise much exceeds that of the quantization levels. This means that theusual equidistant quantization for image sensors may be suboptimal.

The compression comprises applying a non-linear grey valuetransformation on the sensor signals. The non-linear grey valuetransformation may be configured as described in Jähne, B. andSchwarzbauer, M., “Noise equalisation and quasi loss-less image datacompression—or how many bits needs an image sensor?” tm—TechnischesMessen, 83, 16-24, doi: 10.1515/teme-2015-0093, published online 17 Dec.2015, 2016. The non-linear grey value transformation may allow imagecompressing from the first bit depth, e.g. 10 bit, to the lower secondbit depth, e.g. 8 bit, and at the same time to minimize loss ofinformation, in particular in signal range, resolution, and/or contrast.

The term “grey value”, also denoted as grey level, as used herein is abroad term and is to be given its ordinary and customary meaning to aperson of ordinary skill in the art and is not to be limited to aspecial or customized meaning. The term specifically may refer, withoutlimitation, to an indication of brightness of a pixel. The maximum greyvalue may depend on the bit depth.

Without to be bound by this theory, the variance σ² _(g)(g) of thetemporal noise may strongly depend on the grey value g. A relationshipbetween variance and a mean value of the grey value may be described bythe so-called photon transfer curve. By means of non-linear grey valuetransformation the temporal noise can be modified in such a way that thestandard deviation of the temporal noise becomes independent of the greyvalue, resulting in a noise-equilibrated signal. As described in Jähne,B.: Digitale Bildverarbeitung, Springer, Berlin, 6 edn., doi:10.1007/b138991, 2005, it is known from laws governing the propagationof errors, that the variance of a non-linear function h(g) results, inthe first order, to

$\begin{matrix}{\sigma_{h}^{2} \approx {\left( \frac{dh}{dg} \right)^{2}{{\sigma_{g}^{2}(g)}.}}} & (1)\end{matrix}$

If σ² _(h) is to a constant value, formula (1) can be transformed to

${dh} = {\frac{\sigma_{h}}{\sqrt{\sigma^{2}(g)}}d{g.}}$

and integration results in

$\begin{matrix}{{h(g)} = {\sigma_{h}{\overset{g}{\int\limits_{0}}{\frac{dg^{\prime}}{\sqrt{\sigma^{2}\left( g^{\prime} \right)}}.}}}} & (2)\end{matrix}$

The integration constant may be chosen so that h(0)=0. Equation (2)expresses that there is an analytical solution for each function σ²_(g)(g) for which the integral can be solved. Otherwise, it may bepossible to integrate numerically.

With a linear camera, the variance of the temporal noise increaseslinearly with the grey value g:

σ² _(g)(g)=σ² ₀+Kg  (3)

Here the variance of the dark noise is σ₀ ² in DN and K is theamplification of the camera in DN/electron, see EMVA Standard1288—Standard for Characterization of Image Sensors and Cameras, Release3.1, open standard, European Machine Vision Association, doi:10.5281/zenodo.235942, 2016. With the linear variance function (3) theintegral in (2) calculated to be

$\begin{matrix}{{h(g)} = {\frac{2\sigma_{h}}{K}{\left( {\sqrt{\sigma_{0}^{2} + K_{g}} - \sigma_{0}} \right).}}} & (4)\end{matrix}$

The free parameter σ_(h) and the constant standard deviation of thetemporal noise in the non-linearly transformed signal h(g) can be usedin order to determine the signal range [0, h_(max)], and/or a necessarynumber of bits for the compressed signal. With

σ_(max) ²=σ² _(g)(g _(max))  (5)

equation (3) can be rewritten as follows

σ_(g) ²(g)=σ₀ ²+(σ_(max) ²−σ₀ ²)g/g _(max).  (6)

Wherein g max is a maximum grey value. This results in the condition h(gmax)=h max and

$\begin{matrix}{\frac{h}{h_{\max}} = {{\frac{\sqrt{\sigma_{0}^{2} + {\left( {\sigma_{\max}^{2} - \sigma_{0}^{2}} \right)g/g_{\max}}} - \sigma_{0}}{\sigma_{\max} - \sigma_{0}}{with}\sigma_{h}} = {\frac{h_{\max}}{2} \cdot {\frac{\sigma_{\max} + \sigma_{0}}{g_{\max}}.}}}} & (7)\end{matrix}$

Thus, it may be possible to calculate how many bits are necessary forsuitable quantization of a noise-equilibrated signal:

$\begin{matrix}{h_{\max} = {{2{\sigma_{h} \cdot \frac{g_{\max}}{\sigma_{\max} + \sigma_{0}}}} = {{2{\sigma_{h} \cdot {SNR}_{\max}}/\left( {1 + \frac{\sigma_{0}}{\sigma_{\max}}} \right)} \approx {2{\sigma_{h} \cdot {{SNR}_{\max}.}}}}}} & (8)\end{matrix}$

The approximation on the right-hand side can be used becauseσ_(max)>>σ₀. Therefore, the maximum (signal to noise ratio) SNR of thecamera may determine how many bits are required for a sufficientquantization independently of the dark noise. From this equation it canbe derived how many bits are required to quantize the equalized signalh. This is given by the value of h_(max). σ_(h) may be between 0.5and 1. This may allow an optimum quantization, see Jähne, B. andSchwarzbauer, M., “Noise equalisation and quasi loss-less image datacompression—or how many bits needs an image sensor?” tm—TechnischesMessen, 83, 16-24, doi: 10.1515/teme-2015-0093, published online 17 Dec.2015, 2016.

These considerations show that it is possible to use a non-linear greyvalue transformation for compression of image data of a camera, inparticular of a linear camera with a maximum SNR of <126 and aquantization of 8 bit with σ_(h)=1 by means of a non-linear grey valuetransformation. In this way, the entire signal range of the camera canbe covered.

The non-linear grey value transformation h(g) may be applied using atleast one pre-determined lookup table of the non-linear grey valuetransformation h as a function of the grey value g, wherein g is thegrey value of a pixel with the higher bit depth. For example, in case ofcompression to 8 bit, depending on the resolution of the camera, thelookup table may have 2¹⁰ or 2¹² values with 8 bit resolution.

For example, the non-linear grey value transformation may compriseapplying on the grey value g the non-linear grey value transformation asdescribed in equation (4). Using this formula may be advantageousbecause the required parameters can be gained directly from EMVAStandard 1288—(Standard for Characterization of Image Sensors andCameras, Release 3.1, open standard, European Machine VisionAssociation, doi: 10.5281/zenodo. 235942, 2016) measurements, the darknoise σ₀ in DN (in the EMVA 1288 Standard it is called σ_(y,dark)) andthe amplification K. Both parameters result from the offset and thegradient of the photon transfer curve. For the photon transfer curve noabsolute radiometric measurement may be necessary, because the varianceof the temporal noise is plotted as a function of the photo-induced meangrey value.

In particular, the non-linear grey value transformation may compriseapplying on the grey value g the non-linear grey value transformation

${h(g)} = {h_{0} + {\frac{2\sigma_{g}}{\kappa}\left( {\sqrt{\sigma_{0}^{2} + {\kappa\left( {g - g_{off}} \right)}} - \sigma_{0}} \right)}}$

with σ_(g) being a standard deviation of the sensor signal, K being acamera system gain, σ₀ ² being a dark noise, g_(off) being apre-determined offset for g and h, being a pre-determined offset for h.Using this formula may ensure the correct choice of the offsets for Band h, since on account of the temporal noise, values might be obtainedwhich fall below the mean dark value. g₀ may be chosen in such a waythat, first of all, the mean dark value μ_(g,dark) of the camera istaken into account and then an additional value g₀ is subtracted, whichresults in g_(offs)=μ_(g,dark)+g₀.

The term “evaluation device” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art and is not to be limited to a special or customized meaning.The term specifically may refer, without limitation, to an arbitrarydevice adapted to perform the named operations, preferably by using atleast one data processing device and, more preferably, by using at leastone processor and/or at least one application-specific integratedcircuit. Thus, as an example, the at least one evaluation device maycomprise at least one data processing device having a software codestored thereon comprising a number of computer commands. The evaluationdevice may provide one or more hardware elements for performing one ormore of the named operations and/or may provide one or more processorswith software running thereon for performing one or more of the namedoperations. Operations, including evaluating of images. Specifically thedetermining the beam profile and indication of the surface, may beperformed by the at least one evaluation device. Thus, as an example,one or more instructions may be implemented in software and/or hardware.Thus, as an example, the evaluation device may comprise one or moreprogrammable devices such as one or more computers, application-specificintegrated circuits (ASICs), Digital Signal Processors (DSPs), or FieldProgrammable Gate Arrays (FPGAs) which are configured to perform theabove-mentioned evaluation. Additionally or alternatively, however, theevaluation device may also fully or partially be embodied by hardware.

The evaluation device may be or may comprise one or more integratedcircuits, such as one or more application-specific integrated circuits(ASICs), and/or one or more data processing devices, such as one or morecomputers, preferably one or more microcomputers and/ormicrocontrollers, Field Programmable Arrays, or Digital SignalProcessors. Additional components may be comprised, such as one or morepreprocessing devices and/or data acquisition devices, such as one ormore devices for receiving and/or preprocessing of the sensor signals,such as one or more AD-converters and/or one or more filters. Further,the evaluation device may comprise one or more measurement devices, suchas one or more measurement devices for measuring electrical currentsand/or electrical voltages. Further, the evaluation device may compriseone or more data storage devices. Further, the evaluation device maycomprise one or more interfaces, such as one or more wireless interfacesand/or one or more wire-bound interfaces.

The evaluation device can be connected to or may comprise at least onefurther data processing device that may be used for one or more ofdisplaying, visualizing, analyzing, distributing, communicating orfurther processing of information, such as information obtained by theoptical sensor and/or by the evaluation device. The data processingdevice, as an example, may be connected or incorporate at least one of adisplay, a projector, a monitor, an LCD, a TFT, a loudspeaker, amultichannel sound system, an LED pattern, or a further visualizationdevice. It may further be connected or incorporate at least one of acommunication device or communication interface, a connector or a port,capable of sending encrypted or unencrypted information using one ormore of email, text messages, telephone, Bluetooth, Wi-Fi, infrared orinternet interfaces, ports or connections. It may further be connectedto or incorporate at least one of a processor, a graphics processor, aCPU, an Open Multimedia Applications Platform (OMAP™), an integratedcircuit, a system on a chip such as products from the Apple A series orthe Samsung S3C2 series, a microcontroller or microprocessor, one ormore memory blocks such as ROM, RAM, EEPROM, or flash memory, timingsources such as oscillators or phase-locked loops, counter-timers,real-time timers, or power-on reset generators, voltage regulators,power management circuits, or DMA controllers. Individual units mayfurther be connected by buses such as AMBA buses or be integrated in anInternet of Things or Industry 4.0 type network.

The evaluation device and/or the data processing device may be connectedby or have further external interfaces or ports such as one or more ofserial or parallel interfaces or ports, USB, Centronics Port, FireWire,HDMI, Ethernet, Bluetooth, RFID, Wi-Fi, USART, or SPI, or analogueinterfaces or ports such as one or more of ADCs or DACs, or standardizedinterfaces or ports to further devices such as a 2D-camera device usingan RGB-interface such as CameraLink. The evaluation device and/or thedata processing device may further be connected by one or more ofinterprocessor interfaces or ports, FPGA-FPGA-interfaces, or serial orparallel interfaces ports. The evaluation device and the data processingdevice may further be connected to one or more of an optical disc drive,a CD-RW drive, a DVD+RW drive, a flash drive, a memory card, a diskdrive, a hard disk drive, a solid state disk or a solid state hard disk.

The evaluation device and/or the data processing device may be connectedby or have one or more further external connectors such as one or moreof phone connectors, RCA connectors, VGA connectors, hermaphroditeconnectors, USB connectors, HDMI connectors, 8P8C connectors, BCNconnectors, IEC 60320 C14 connectors, optical fiber connectors,D-subminiature connectors, RF connectors, coaxial connectors, SCARTconnectors, XLR connectors, and/or may incorporate at least one suitablesocket for one or more of these connectors.

In step c), the evaluation step comprises evaluating the compressedreflection image by using the evaluation device. The evaluationcomprises determining at least one longitudinal coordinate for thereflection feature by analysis of its respective beam profile.

The analysis of the beam profile may comprises determining at least onefirst area and at least one second area of the beam profile. Theanalysis of the beam profile may further comprises deriving a combinedsignal Q by one or more of dividing the first area and the second area,dividing multiples of the first area and the second area, dividinglinear combinations of the first area and the second area. The analysisof the beam profile may further comprises using at least onepredetermined relationship between the combined signal Q and thelongitudinal coordinate for determining the longitudinal coordinate.

The evaluation device may be configured for identifying and/or selectingthe reflection features in the compressed reflection image. Theevaluation device may be configured for performing at least one imageanalysis and/or image processing for identifying and/or selecting thereflection features. The image analysis and/or image processing may useat least one feature detection algorithm. The image analysis and/orimage processing may comprise one or more of the following: a filtering;a selection of at least one region of interest; a formation of adifference image between an image created by the sensor signals and atleast one offset; an inversion of sensor signals by inverting an imagecreated by the sensor signals; a formation of a difference image betweenan image created by the sensor signals at different times; a backgroundcorrection; a decomposition into color channels; a decomposition intohue; saturation; and brightness channels; a frequency decomposition; asingular value decomposition; applying a blob detector; applying acorner detector; applying a Determinant of Hessian filter; applying aprinciple curvature-based region detector; applying a maximally stableextremal regions detector; applying a generalized Hough-transformation;applying a ridge detector; applying an affine invariant featuredetector; applying an affine-adapted interest point operator; applying aHarris affine region detector; applying a Hessian affine regiondetector; applying a scale-invariant feature transform; applying ascale-space extrema detector; applying a local feature detector;applying speeded up robust features algorithm; applying a gradientlocation and orientation histogram algorithm; applying a histogram oforiented gradients descriptor; applying a Deriche edge detector;applying a differential edge detector; applying a spatio-temporalinterest point detector; applying a Moravec corner detector; applying aCanny edge detector; applying a Laplacian of Gaussian filter; applying aDifference of Gaussian filter; applying a Sobel operator; applying aLaplace operator; applying a Scharr operator; applying a Prewittoperator; applying a Roberts operator; applying a Kirsch operator;applying a high-pass filter; applying a low-pass filter; applying aFourier transformation; applying a Radon-transformation; applying aHough-transformation; applying a wavelet-transformation; a thresholding;creating a binary image. Specifically, the evaluation of the compressedreflection image may comprise selecting at least one region of interestin the reflection image. The region of interest may be determinedmanually by a user or may be determined automatically, such as byrecognizing an object within an image generated by the sensor element.For example, in case of a spot-like reflection feature the region ofinterest may be selected as a region around the spot profile.

The evaluation device may be configured for performing at least oneimage correction. The image correction may comprise at least onebackground subtraction. The evaluation device may be adapted to removeinfluences from background light from the respective beam profile, forexample, by an imaging without further illumination.

The evaluation device is configured for determining a longitudinalcoordinate for the reflection feature, e.g. for each of the reflectionfeatures, of the compressed reflection image by analysis of its beamprofile. As used herein, the term “analysis of the beam profile” maygenerally refer to evaluating of the beam profile and may comprise atleast one mathematical operation and/or at least one comparison and/orat least symmetrizing and/or at least one filtering and/or at least onenormalizing. For example, the analysis of the beam profile may compriseat least one of a histogram analysis step, a calculation of a differencemeasure, application of a neural network, application of a machinelearning algorithm. The evaluation device may be configured forsymmetrizing and/or for normalizing and/or for filtering the beamprofile, in particular to remove noise or asymmetries from recordingunder larger angles, recording edges or the like. The evaluation devicemay filter the beam profile by removing high spatial frequencies such asby spatial frequency analysis and/or median filtering or the like.Summarization may be performed by center of intensity of the light spotand averaging all intensities at the same distance to the center. Theevaluation device may be configured for normalizing the beam profile toa maximum intensity, in particular to account for intensity differencesdue to the recorded distance. The evaluation device may be configuredfor removing influences from background light from the beam profile, forexample, by an imaging without illumination.

The reflection feature may cover or may extend over at least one pixelof the image. For example, the reflection feature may cover or mayextend over plurality of pixels. The evaluation device may be configuredfor determining and/or for selecting all pixels connected to and/orbelonging to the reflection feature, e.g. a light spot. The evaluationdevice may be configured for determining the center of intensity by

${R_{coi} = \frac{1}{l \cdot {\sum{j \cdot r_{pixel}}}}},$

wherein R_(coi) is a position of center of intensity, r_(pixel) is thepixel position and l=Σ_(j)I_(total) with j being the number of pixels jconnected to and/or belonging to the reflection feature and I_(total)being the total intensity.

The evaluation device may be configured for determining the longitudinalcoordinate for the reflection features by using adepth-from-photon-ratio technique, also denoted as beam profileanalysis. With respect to depth-from-photon-ratio (DPR) techniquereference is made to WO 2018/091649 A1, WO 2018/091638 A1, WO2018/091640 A1 and C. Lennartz, F. Schick, S. Metz, “Whitepaper—BeamProfile Analysis for 3D imaging and material detection” Apr. 28, 2021,Ludwigshafen, Germany, the full content of which is included byreference.

The evaluation device may be configured for determining the beam profileof the reflection feature. As used herein, the term “determining thebeam profile” refers to identifying and/or selecting at least onereflection feature within the compressed reflection image and evaluatingat least one intensity distribution of the reflection feature. As anexample, a region of the compressed reflection image may be used andevaluated for determining the intensity distribution, such as athree-dimensional intensity distribution or a two-dimensional intensitydistribution, such as along an axis or line through the compressedreflection image. As an example, a center of illumination by the lightbeam may be determined, such as by determining the at least one pixelhaving the highest illumination, and a cross-sectional axis may bechosen through the center of illumination. The intensity distributionmay an intensity distribution as a function of a coordinate along thiscross-sectional axis through the center of illumination. Otherevaluation algorithms are feasible.

The analysis of the beam profile may comprise determining at least onefirst area and at least one second area of the beam profile. The firstarea of the beam profile may be an area A1 and the second area of thebeam profile may be an area A2. The evaluation device may be configuredfor integrating the first area and the second area. The evaluationdevice may be configured to derive a combined signal Q, also denoted asquotient Q, by one or more of dividing the integrated first area and theintegrated second area, dividing multiples of the integrated first areaand the integrated second area, dividing linear combinations of theintegrated first area and the integrated second area.

The evaluation device may configured for determining at least two areasof the beam profile and/or to segment the beam profile in at least twosegments comprising different areas of the beam profile, whereinoverlapping of the areas may be possible as long as the areas are notcongruent. For example, the evaluation device may be configured fordetermining a plurality of areas such as two, three, four, five, or upto ten areas. The evaluation device may be configured for segmenting thelight spot into at least two areas of the beam profile and/or to segmentthe beam profile in at least two segments comprising different areas ofthe beam profile. The evaluation device may be configured fordetermining for at least two of the areas an integral of the beamprofile over the respective area. The evaluation device may beconfigured for comparing at least two of the determined integrals.Specifically, the evaluation device may be configured for determining atleast one first area and at least one second area of the beam profile.As used herein, the term “area of the beam profile” generally refers toan arbitrary region of the beam profile at the position of the opticalsensor used for determining the combined signal. The first area of thebeam profile and the second area of the beam profile may be one or bothof adjacent or overlapping regions. The first area of the beam profileand the second area of the beam profile may be not congruent in area.For example, the evaluation device may be configured for dividing asensor region of the first sensor element into at least two sub-regions,wherein the evaluation device may be configured for dividing the sensorregion of the first sensor element into at least one left part and atleast one right part and/or at least one upper part and at least onelower part and/or at least one inner and at least one outer part. Theevaluation device may be adapted to integrate the first area and thesecond area.

The first area of the beam profile may comprise essentially edgeinformation of the beam profile and the second area of the beam profilecomprises essentially center information of the beam profile, and/or thefirst area of the beam profile may comprise essentially informationabout a left part of the beam profile and the second area of the beamprofile comprises essentially information about a right part of the beamprofile. The beam profile may have a center, i.e. a maximum value of thebeam profile and/or a center point of a plateau of the beam profileand/or a geometrical center of the light spot, and falling edgesextending from the center. The second region may comprise inner regionsof the cross section and the first region may comprise outer regions ofthe cross section. As used herein, the term “essentially centerinformation” generally refers to a low proportion of edge information,i.e. proportion of the intensity distribution corresponding to edges,compared to a proportion of the center information, i.e. proportion ofthe intensity distribution corresponding to the center. Preferably, thecenter information has a proportion of edge information of less than10%, more preferably of less than 5%, most preferably the centerinformation comprises no edge content. As used herein, the term“essentially edge information” generally refers to a low proportion ofcenter information compared to a proportion of the edge information. Theedge information may comprise information of the whole beam profile, inparticular from center and edge regions. The edge information may have aproportion of center information of less than 10%, preferably of lessthan 5%, more preferably the edge information comprises no centercontent. At least one area of the beam profile may be determined and/orselected as second area of the beam profile if it is close or around thecenter and comprises essentially center information. At least one areaof the beam profile may be determined and/or selected as first area ofthe beam profile if it comprises at least parts of the falling edges ofthe cross section. For example, the whole area of the cross section maybe determined as first region.

Other selections of the first area A1 and second area A2 may befeasible. For example, the first area may comprise essentially outerregions of the beam profile and the second area may comprise essentiallyinner regions of the beam profile. For example, in case of atwo-dimensional beam profile, the beam profile may be divided in a leftpart and a right part, wherein the first area may comprise essentiallyareas of the left part of the beam profile and the second area maycomprise essentially areas of the right part of the beam profile.

The edge information may comprise information relating to a number ofphotons in the first area of the beam profile and the center informationmay comprise information relating to a number of photons in the secondarea of the beam profile. The evaluation device may be configured fordetermining an area integral of the beam profile. The evaluation devicemay be configured for determining the edge information by integratingand/or summing of the first area. The evaluation device may beconfigured for determining the center information by integrating and/orsumming of the second area. For example, the beam profile may be atrapezoid beam profile and the evaluation device may be configured fordetermining an integral of the trapezoid. Further, when trapezoid beamprofiles may be assumed, the determination of edge and center signalsmay be replaced by equivalent evaluations making use of properties ofthe trapezoid beam profile such as determination of the slope andposition of the edges and of the height of the central plateau andderiving edge and center signals by geometric considerations.

In one embodiment, A1 may correspond to a full or complete area of afeature point. A2 may be a central area of the feature point. Thecentral area may be a constant value. The central area may be smallercompared to the full area of the feature point. For example, in case ofa circular feature point, the central area may have a radius from 0.1 to0.9 of a full radius of the feature point, preferably from 0.4 to 0.6 ofthe full radius.

In one embodiment, the illumination pattern may comprise at least pointpattern. A1 may correspond to an area with a full radius of a point ofthe point pattern on the optical sensors. A2 may be a central area ofthe point in the point pattern on the optical sensors. The central areamay be a constant value. The central area may have a radius compared tothe full radius. For example, the central area may have a radius from0.1 to 0.9 of the full radius, preferably from 0.4 to 0.6 of the fullradius.

The evaluation device may be configured to derive the quotient Q by oneor more of dividing the first area and the second area, dividingmultiples of the first area and the second area, dividing linearcombinations of the first area and the second area. The evaluationdevice may be configured for deriving the quotient Q by

$Q = \frac{\int{\int_{A1}{{E\left( {x,y} \right)}{dxdy}}}}{\int{\int_{A2}{{E\left( {x,y} \right)}{dxdy}}}}$

wherein x and y are transversal coordinates, A1 and A2 are the first andsecond area of the beam profile, respectively, and E(x,y) denotes thebeam profile.

Additionally or alternatively, the evaluation device may be adapted todetermine one or both of center information or edge information from atleast one slice or cut of the light spot. This may be realized, forexample, by replacing the area integrals in the quotient Q by a lineintegral along the slice or cut. For improved accuracy, several slicesor cuts through the light spot may be used and averaged. In case of anelliptical spot profile, averaging over several slices or cuts mayresult in improved distance information.

For example, the evaluation device may be configured for evaluating thebeam profile, by

-   -   determining the pixel having the highest sensor signal and        forming at least one center signal;    -   evaluating sensor signals of the matrix and forming at least one        sum signal;    -   determining the quotient Q by combining the center signal and        the sum signal; and    -   determining at least one longitudinal coordinate z of the object        by evaluating the quotient Q.

The sensor signal may be a signal generated by the optical sensor and/orat least one pixel of the optical sensor in response to illumination.Specifically, the sensor signal may be or may comprise at least oneelectrical signal, such as at least one analogue electrical signaland/or at least one digital electrical signal. More specifically, thesensor signal may be or may comprise at least one voltage signal and/orat least one current signal. More specifically, the sensor signal maycomprise at least one photocurrent. Further, either raw sensor signalsmay be used, or the detector, the optical sensor or any other elementmay be adapted to process or preprocess the sensor signal, therebygenerating secondary sensor signals, which may also be used as sensorsignals, such as preprocessing by filtering or the like. The term“center signal” generally refers to the at least one sensor signalcomprising essentially center information of the beam profile. As usedherein, the term “highest sensor signal” refers to one or both of alocal maximum or a maximum in a region of interest. For example, thecenter signal may be the signal of the pixel having the highest sensorsignal out of the plurality of sensor signals generated by the pixels ofthe entire matrix or of a region of interest within the matrix, whereinthe region of interest may be predetermined or determinable within animage generated by the pixels of the matrix. The center signal may arisefrom a single pixel or from a group of optical sensors, wherein, in thelatter case, as an example, the sensor signals of the group of pixelsmay be added up, integrated or averaged, in order to determine thecenter signal. The group of pixels from which the center signal arisesmay be a group of neighboring pixels, such as pixels having less than apredetermined distance from the actual pixel having the highest sensorsignal, or may be a group of pixels generating sensor signals beingwithin a predetermined range from the highest sensor signal. The groupof pixels from which the center signal arises may be chosen as large aspossible in order to allow maximum dynamic range. The evaluation devicemay be adapted to determine the center signal by integration of theplurality of sensor signals, for example the plurality of pixels aroundthe pixel having the highest sensor signal. For example, the beamprofile may be a trapezoid beam profile and the evaluation device may beadapted to determine an integral of the trapezoid, in particular of aplateau of the trapezoid.

As outlined above, the center signal generally may be a single sensorsignal, such as a sensor signal from the pixel in the center of thelight spot, or may be a combination of a plurality of sensor signals,such as a combination of sensor signals arising from pixels in thecenter of the light spot, or a secondary sensor signal derived byprocessing a sensor signal derived by one or more of the aforementionedpossibilities. The determination of the center signal may be performedelectronically, since a comparison of sensor signals is fairly simplyimplemented by conventional electronics, or may be performed fully orpartially by software. Specifically, the center signal may be selectedfrom the group consisting of: the highest sensor signal; an average of agroup of sensor signals being within a predetermined range of tolerancefrom the highest sensor signal; an average of sensor signals from agroup of pixels containing the pixel having the highest sensor signaland a predetermined group of neighboring pixels; a sum of sensor signalsfrom a group of pixels containing the pixel having the highest sensorsignal and a predetermined group of neighboring pixels; a sum of a groupof sensor signals being within a predetermined range of tolerance fromthe highest sensor signal; an average of a group of sensor signals beingabove a predetermined threshold; a sum of a group of sensor signalsbeing above a predetermined threshold; an integral of sensor signalsfrom a group of optical sensors containing the optical sensor having thehighest sensor signal and a predetermined group of neighboring pixels;an integral of a group of sensor signals being within a predeterminedrange of tolerance from the highest sensor signal; an integral of agroup of sensor signals being above a predetermined threshold.

Similarly, the term “sum signal” generally refers to a signal comprisingessentially edge information of the beam profile. For example, the sumsignal may be derived by adding up the sensor signals, integrating overthe sensor signals or averaging over the sensor signals of the entirematrix or of a region of interest within the matrix, wherein the regionof interest may be predetermined or determinable within an imagegenerated by the optical sensors of the matrix. When adding up,integrating over or averaging over the sensor signals, the actualoptical sensors from which the sensor signal is generated may be leftout of the adding, integration or averaging or, alternatively, may beincluded into the adding, integration or averaging. The evaluationdevice may be adapted to determine the sum signal by integrating signalsof the entire matrix, or of the region of interest within the matrix.For example, the beam profile may be a trapezoid beam profile and theevaluation device may be adapted to determine an integral of the entiretrapezoid. Further, when trapezoid beam profiles may be assumed, thedetermination of edge and center signals may be replaced by equivalentevaluations making use of properties of the trapezoid beam profile suchas determination of the slope and position of the edges and of theheight of the central plateau and deriving edge and center signals bygeometric considerations.

Similarly, the center signal and edge signal may also be determined byusing segments of the beam profile such as circular segments of the beamprofile. For example, the beam profile may be divided into two segmentsby a secant or a chord that does not pass the center of the beamprofile. Thus, one segment will essentially contain edge information,while the other segment will contain essentially center information. Forexample, to further reduce the amount of edge information in the centersignal, the edge signal may further be subtracted from the centersignal.

The combined signal Q, also denoted as quotient Q, may be a signal whichis generated by combining the center signal and the sum signal.Specifically, the determining may include one or more of: forming aquotient of the center signal and the sum signal or vice versa; forminga quotient of a multiple of the center signal and a multiple of the sumsignal or vice versa; forming a quotient of a linear combination of thecenter signal and a linear combination of the sum signal or vice versa.Additionally or alternatively, the quotient Q may comprise an arbitrarysignal or signal combination which contains at least one item ofinformation on a comparison between the center signal and the sumsignal.

The term “longitudinal coordinate for the reflection feature” as usedherein is a broad term and is to be given its ordinary and customarymeaning to a person of ordinary skill in the art and is not to belimited to a special or customized meaning. The term specifically mayrefer, without limitation, to a distance between the camera and theobject. The evaluation device may be configured for using the at leastone predetermined relationship between the combined signal and thelongitudinal coordinate for determining the longitudinal coordinate. Thepredetermined relationship may be one or more of an empiricrelationship, a semi-empiric relationship and an analytically derivedrelationship. The evaluation device may comprise at least one datastorage device for storing the predetermined relationship, such as alookup list or a lookup table.

The evaluation device may be configured for executing at least onedepth-from-photon-ratio algorithm which computes distances for thereflection features with zero order and higher order.

The evaluation step may further comprise determining at least onematerial property of the object by analysis of the beam profile of thereflection feature.

The term “material property” as used herein is a broad term and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art and is not to be limited to a special or customized meaning.The term specifically may refer, without limitation, to at least onearbitrary property of a material configured for characterizing and/oridentification and/or classification of the material. For example, thematerial property may be a property selected from the group consistingof: roughness, penetration depth of light into the material, a propertycharacterizing the material as biological or non-biological material, areflectivity, a specular reflectivity, a diffuse reflectivity, a surfaceproperty, a measure for translucence, a scattering, specifically aback-scattering behavior or the like. The at least one material propertymay be a property selected from the group consisting of: a scatteringcoefficient, a translucency, a transparency, a deviation from aLambertian surface reflection, a speckle, and the like.

The term “determining at least one material property” as used herein isa broad term and is to be given its ordinary and customary meaning to aperson of ordinary skill in the art and is not to be limited to aspecial or customized meaning. The term specifically may refer, withoutlimitation, to one or more of identifying, characterizing, and assigningthe material property to the object. The detector may comprise at leastone database comprising a list and/or table, such as a lookup list or alookup table, of predefined and/or predetermined material properties.The list and/or table of material properties may be determined and/orgenerated by performing at least one test measurement using thedetector, for example by performing material tests using samples havingknown material properties. The list and/or table of material propertiesmay be determined and/or generated at the manufacturer site and/or bythe user of the detector. The material property may additionally beassigned to a material classifier such as one or more of a materialname, a material group such as biological or non-biological material,translucent or non-translucent materials, metal or non-metal, skin ornon-skin, fur or non-fur, carpet or non-carpet, reflective ornon-reflective, specular reflective or non-specular reflective, foam ornon-foam, hair or non-hair, roughness groups or the like. The detectormay comprise at least one database comprising a list and/or tablecomprising the material properties and associated material name and/ormaterial group.

The object may comprise at least one surface on which the illuminationpattern is projected. The surface may be adapted to at least partiallyreflect the illumination pattern back towards the detector. For example,without wishing to be bound by this theory, human skin may have areflection profile, also denoted back scattering profile, comprisingparts generated by back reflection of the surface, denoted as surfacereflection, and parts generated by very diffuse reflection from lightpenetrating the skin, denoted as diffuse part of the back reflection.With respect to reflection profile of human skin reference is made to“Lasertechnik in der Medizin: Grundlagen, Systeme, Anwendungen”,“Wirkung von Laserstrahlung auf Gewebe”, 1991, pages 171 to 266, JurgenEichler, Theo Seiler, Springer Verlag, ISBN 0939-0979. The surfacereflection of the skin may increase with the wavelength increasingtowards the near infrared. Further, the penetration depth may increasewith increasing wavelength from visible to near infrared. The diffusepart of the back reflection may increase with penetrating depth of thelight. These material properties may be used to distinguish skin fromother materials, specifically by analyzing the back scattering profile.

The determining of the material property may be performed using beamprofile analysis. Specifically, beam profile analysis makes use ofreflection properties of coherent light projected onto object surfacesto classify materials. The determining of the material property may beperformed as described in one of WO 2020/187719, WO 2021/170791 A1and/or WO 2021/152070, the full content of which is included byreference. Specifically, analyzing of the beam profile of the reflectionfeature may be performed by feature-based methods. Additionally oralternatively, machine learning methods may be used. The feature basedmethods may be used in combination with machine learning methods whichmay allow parametrization of a classification model. Alternatively or incombination, convolutional neuronal networks may be utilized to classifymaterials by using the reflection image as an input.

For example, the evaluation device may be configured for characterizinga reflection feature as being generated by a material, e.g. human skin,in case its reflection beam profile fulfills at least one predeterminedor predefined criterion. As used herein, the term “at least onepredetermined or predefined criterion” refers to at least one propertyand/or value suitable to distinguish biological tissue, in particularhuman skin, from other materials. The predetermined or predefinedcriterion may be or may comprise at least one predetermined orpredefined value and/or threshold and/or threshold range referring to amaterial property. The reflection feature may be indicated as to begenerated by the material, e.g. human skin, in case the reflection beamprofile fulfills the at least one predetermined or predefined criterion.The evaluation device may be configured for characterizing thereflection feature as to be non-skin otherwise. For example, the objectmay be a biological tissue, e.g. skin. As used herein, the term“biological tissue” generally refers to biological material comprisingliving cells. Specifically, the evaluation device may be configured forskin detection. The evaluation device may be configured forcharacterizing the object as being generated by biological tissue, inparticular human skin. The term “characterizing” of being generated bybiological tissue, in particular human skin, may refer to determiningand/or validating whether a surface to be examined or under test is orcomprises biological tissue, in particular human skin, and/or todistinguish biological tissue, in particular human skin, from othertissues, in particular other surfaces. The evaluation device may beconfigured for distinguishing human skin from one or more of inorganictissue, metal surfaces, plastics surfaces, foam, paper, wood, a display,a screen, cloth. The evaluation device may be configured fordistinguishing human biological tissue from surfaces of artificial ornon-living objects.

The reflection properties of skin may be characterized by thesimultaneous occurrence of direct reflection at the surface(Lambertian-like) and subsurface scattering (volume scattering). Thismay lead to a broadening of the laser spot on skin compared to theabove-mentioned materials. For example, without wishing to be bound bytheory, the object, e.g. human skin, may have a reflection profile, alsodenoted back scattering profile. The reflection profile may compriseparts generated by back reflection of the surface, denoted as surfacereflection, and parts generated by very diffuse reflection from lightpenetrating the skin, denoted as diffuse part of the back reflection.With respect to reflection profile of human skin reference is made to“Lasertechnik in der Medizin: Grundlagen, Systeme, Anwendungen”,“Wirkung von Laserstrahlung auf Gewebe”, 1991, pages 171 to 266, JurgenEichler, Theo Seiler, Springer Verlag, ISBN 0939-0979. The surfacereflection of the skin may increase with the wavelength increasingtowards the near infrared. Further, the penetration depth may increasewith increasing wavelength from visible to near infrared. The diffusepart of the back reflection may increase with penetrating depth of thelight. These properties may be used to distinguish skin from othermaterials, by analyzing the back scattering profile.

Specifically, the evaluation device may be configured for comparing thereflection beam profile with at least one predetermined and/orprerecorded and/or predefined beam profile. The predetermined and/orprerecorded and/or predefined beam profile may be stored in a table or alookup table and may be determined e.g. empirically, and may, as anexample, be stored in at least one data storage device of the imagingdevice. For example, the predetermined and/or prerecorded and/orpredefined beam profile may be determined during initial start-up of adevice executing the method according to the present invention. Forexample, the predetermined and/or prerecorded and/or predefined beamprofile may be stored in at least one data storage device of theevaluation device, e.g. by software, specifically by the app downloadedfrom an app store or the like. The reflection feature may be identifiedas to be generated by a material, e.g. biological tissue, in case thereflection beam profile and the predetermined and/or prerecorded and/orpredefined beam profile are identical. The comparison may compriseoverlaying the reflection beam profile and the predetermined orpredefined beam profile such that their centers of intensity match. Thecomparison may comprise determining a deviation, e.g. a sum of squaredpoint to point distances, between the reflection beam profile and thepredetermined and/or prerecorded and/or predefined beam profile. Theevaluation device may be adapted to compare the determined deviationwith at least one threshold, wherein in case the determined deviation isbelow and/or equal the threshold the surface is indicated as biologicaltissue and/or the detection of biological tissue is confirmed. Thethreshold value may be stored in a table or a lookup table and may bedetermined e.g. empirically and may, as an example, be stored in atleast one data storage device of the evaluation device.

Additionally or alternatively, the material property may be determinedby applying at least one image filter to the image of the reflectionfeature. As further used herein, the term “image” refers to atwo-dimensional function, f(x,y), wherein brightness and/or color valuesare given for any x,y-position in the image. The position may bediscretized corresponding to the recording pixels. The brightness and/orcolor may be discretized corresponding to a bitdepth of the opticalsensors. As used herein, the term “image filter” refers to at least onemathematical operation applied to the beam profile and/or to the atleast one specific region of the beam profile. Specifically, the imagefilter ϕ maps an image f, or a region of interest in the image, onto areal number, ϕ(f(x,y))=φ, wherein φ denotes a feature, in particular amaterial feature. Images may be subject to noise and the same holds truefor features. Therefore, features may be random variables. The featuresmay be normally distributed. If features are not normally distributed,they may be transformed to be normally distributed such as by aBox-Cox-Transformation.

The evaluation device may be configured for determining at least onematerial feature ϕ_(2m) by applying at least one material dependentimage filter ϕ₂ to the image. As used herein, the term “materialdependent” image filter refers to an image having a material dependentoutput. The output of the material dependent image filter is denotedherein “material feature φ_(2m)” or “material dependent feature φ_(2m)”.The material feature may be or may comprise at least one informationabout the at least one material property of the surface of the scenehaving generated the reflection feature.

The material dependent image filter may be at least one filter selectedfrom the group consisting of: a luminance filter; a spot shape filter; asquared norm gradient; a standard deviation; a smoothness filter such asa Gaussian filter or median filter; a grey-level-occurrence-basedcontrast filter; a grey-level-occurrence-based energy filter; agrey-level-occurrence-based homogeneity filter; agrey-level-occurrence-based dissimilarity filter; a Law's energy filter;a threshold area filter; or a linear combination thereof; or a furthermaterial dependent image filter ϕ_(2other) which correlates to one ormore of the luminance filter, the spot shape filter, the squared normgradient, the standard deviation, the smoothness filter, thegrey-level-occurrence-based energy filter, thegrey-level-occurrence-based homogeneity filter, thegrey-level-occurrence-based dissimilarity filter, the Law's energyfilter, or the threshold area filter, or a linear combination thereof by|ρϕ_(2other,ϕm)|≥0.40 with ϕ_(m) being one of the luminance filter, thespot shape filter, the squared norm gradient, the standard deviation,the smoothness filter, the grey-level-occurrence-based energy filter,the grey-level-occurrence-based homogeneity filter, thegrey-level-occurrence-based dissimilarity filter, the Law's energyfilter, or the threshold area filter, or a linear combination thereof.The further material dependent image filter ϕ_(2other) may correlate toone or more of the material dependent image filters|ρϕ_(2other,ϕm)|≥0.60, preferably by |ρϕ_(2other,ϕm)|≥0.80.

The material dependent image filter may be at least one arbitrary filterϕ that passes a hypothesis testing. As used herein, the term “passes ahypothesis testing” refers to the fact that a Null-hypothesis H₀ isrejected and an alternative hypothesis H₁ is accepted. The hypothesistesting may comprise testing the material dependency of the image filterby applying the image filter to a predefined data set. The data set maycomprise a plurality of beam profile images. As used herein, the term“beam profile image” refers to a sum of N_(B) Gaussian radial basisfunctions,

${{f_{k}\left( {x,y} \right)} = {❘{{\sum}_{l = 0}^{N_{B} - 1}{g_{lk}\left( {x,y} \right)}}❘}},$g_(lk)(x, y) = a_(lk)e^(−(α(x − x_(lk)))²)e^(−(α(y − y_(lk)))²)

wherein each of the N_(B) Gaussian radial basis functions is defined bya center (x_(lk),y_(lk)), a prefactor, a_(ik), and an exponential factorα=1/ϵ. The exponential factor is identical for all Gaussian functions inall images. The center-positions, x_(lk),y_(lk), are identical for allimages ƒ_(k):(x₀, x₁, . . . , x_(N) _(B-1) ), (y₀, y₁, . . . , y_(N)_(B-1) ). Each of the beam profile images in the dataset may correspondto a material classifier and a distance. The material classifier may bea label such as ‘Material A’, ‘Material B’, etc. The beam profile imagesmay be generated by using the above formula for ƒ_(k)(x,y) incombination with the following parameter table:

Image Material classifier, Distance Index Material Index z Parameters k= 0 Skin, m = 0 0.4 m (a₀₀, a₁₀, . . . , a_(N) _(B) ⁻¹⁰ ₎ k = 1 Skin, m= 0 0.6 m (a₀₁, a₁₁, . . . , a_(N) _(B) ⁻¹¹ ₎ k = 2 Fabric, m = 1 0.6 m(a₀₂, a₁₂, . . . , a_(N) _(B) ⁻¹² ₎ . . . . . . k = N Material J, m = J− 1 (a_(0N), a_(1N), . . . , a_(N) _(B) _(−1N) ₎

The values for x, y, are integers corresponding to pixels with

$\begin{pmatrix}x \\y\end{pmatrix} \in {\left\lbrack {0,1,{\ldots 31}} \right\rbrack^{2}.}$

The images may have a pixel size of 32×32. The dataset of beam profileimages may be generated by using the above formula for ƒ_(k) incombination with a parameter set to obtain a continuous description ofƒ_(k). The values for each pixel in the 32×32-image may be obtained byinserting integer values from 0, . . . , 31 for x, y, in ƒ_(k)(x,y). Forexample, for pixel (6,9), the value ƒ_(k)(6,9) may be computed.

Subsequently, for each image ƒ_(k), the feature value φ_(k)corresponding to the filter ϕ may be calculated, Φ(ƒ_(k)(x,y),z_(k))=φ_(k), wherein z_(k) is a distance value corresponding to theimage ƒ_(k) from the predefined data set. This yields a dataset withcorresponding generated feature values φ_(k). The hypothesis testing mayuse a Null-hypothesis that the filter does not distinguish betweenmaterial classifier. The Null-Hypothesis may be given by H₀: μ₁=μ₂= . .. =μ_(j), wherein μ_(m) is the expectation value of each material-groupcorresponding to the feature values φ_(k). Index m denotes the materialgroup. The hypothesis testing may use as alternative hypothesis that thefilter does distinguish between at least two material classifiers. Thealternative hypothesis may be given by H₁:∃m, m′:μ_(m)≠μ_(m′). As usedherein, the term “not distinguish between material classifiers” refersto that the expectation values of the material classifiers areidentical. As used herein, the term “distinguishes material classifiers”refers to that at least two expectation values of the materialclassifiers differ. As used herein “distinguishes at least two materialclassifiers” is used synonymous to “suitable material classifier”. Thehypothesis testing may comprise at least one analysis of variance(ANOVA) on the generated feature values. In particular, the hypothesistesting may comprise determining a mean-value of the feature values foreach of the J materials, i.e. in total J mean values,

${{\overset{¯}{\varphi}}_{m} = \frac{{\sum}_{i}\varphi_{i,m}}{N_{m}}},$

for m∈[0, 1, . . . , J−1], wherein N_(m) gives the number of featurevalues for each of the j materials in the predefined data set. Thehypothesis testing may comprise determining a mean-value of all Nfeature values

$\overset{¯}{\varphi} = {\frac{{\sum}_{m}{\sum}_{i}\varphi_{i,m}}{N}.}$

The hypothesis testing may comprise determining a Mean Sum Squareswithin:

mssw=(Σ_(m)Σ_(i)(φ_(i,m)−φ _(m))²)/(N−J).

The hypothesis testing may comprise determining a Mean Sum of Squaresbetween,

mssb=(Σ_(m)(φ _(m)−φ)² N _(m)/(J−1).

The hypothesis testing may comprise performing an F-Test:

${{{CDF}(x)} = {I_{\frac{d_{1^{x}}}{{d_{1}x} + d_{2}}}\left( {\frac{d_{1}}{2},\frac{d_{2}}{2}} \right)}},{{{where}d_{1}} = {N - J}},{d_{2} = {J - 1}},$F(x) = 1 − CDF(x) p = F(mssb/mssw)

Herein, I_(x) is the regularized incomplete Beta-Function,

${{I_{x}\left( {a,b} \right)} = \frac{B\left( {{x;a},b} \right)}{B\left( {a,b} \right)}},$

with the Euler Beta-Function B(a,b)=∫₀ ¹t^(a-1)(1−t)^(b-1)dt and B(x;a,b)=∫₀ ^(x)t^(a-1)(1−t)^(b-1)dt being the incomplete Beta-Function. Theimage filter may pass the hypothesis testing if a p-value, p, is smalleror equal than a pre-defined level of significance. The filter may passthe hypothesis testing if p≤0.075, preferably p≤0.05, more preferablyp≤0.025, and most preferably p≤0.01. For example, in case thepre-defined level of significance is α=0.075, the image filter may passthe hypothesis testing if the p-value is smaller than α=0.075. In thiscase the Null-hypothesis H₀ can be rejected and the alternativehypothesis H₁ can be accepted. The image filter thus distinguishes atleast two material classifiers. Thus, the image filter passes thehypothesis testing.

In the following, image filters are described assuming that thereflection image comprises at least one reflection feature, inparticular a spot image. A spot image f may be given by a function ƒ:

→

, wherein the background of the image f may be already subtracted.However, other reflection features may be possible.

For example, the material dependent image filter may be a luminancefilter. The luminance filter may return a luminance measure of a spot asmaterial feature. The material feature may be determined by

${\varphi_{2m} = {{\Phi\left( {f,z} \right)} = {- {\int{{f(x)}{dx}\frac{z^{2}}{d_{ray} \cdot n}}}}}},$

where f is the spot image. The distance of the spot is denoted by z,where z may be obtained for example by using a depth-from-defocus ordepth-from-photon ratio technique and/or by using a triangulationtechnique. The surface normal of the material is given by n∈

and can be obtained as the normal of the surface spanned by at leastthree measured points. The vector d_(ray)∈

is the direction vector of the light source. Since the position of thespot is known by using a depth-from-defocus or depth-from-photon ratiotechnique and/or by using a triangulation technique wherein the positionof the light source is known as a parameter of the imaging device,d_(ray), is the difference vector between spot and light sourcepositions.

For example, the material dependent image filter may be a filter havingan output dependent on a spot shape. This material dependent imagefilter may return a value which correlates to the translucence of amaterial as material feature. The translucence of materials influencesthe shape of the spots. The material feature may be given by

${\varphi_{2m} = {{\Phi(f)} = \frac{\int{{H\left( {{f(x)} - {\alpha h}} \right)}{dx}}}{\int{{H\left( {{f(x)} - {\beta h}} \right)}{dx}}}}},$

wherein 0<α,β<1 are weights for the spot height h, and H denotes theHeavyside function, i.e. H(x)=1:x≥0, H(x)=0:x<0. The spot height h maybe determined by

h=∫ _(B) _(r) ƒ(x)dx,

where B_(r) is an inner circle of a spot with radius r.

For example, the material dependent image filter may be a squared normgradient. This material dependent image filter may return a value whichcorrelates to a measure of soft and hard transitions and/or roughness ofa spot as material feature. The material feature may be defined by

φ_(2m)=ϕ(ƒ)=∫∥∇ƒ(x)∥² dx.

For example, the material dependent image filter may be a standarddeviation. The standard deviation of the spot may be determined by

φ_(2m)=ϕ(ƒ)=∫(ƒ(x)−μ)² dx.

Wherein μ is the mean value given by μ=∫(ƒ(x))dx.

For example, the material dependent image filter may be a smoothnessfilter such as a Gaussian filter or median filter. In one embodiment ofthe smoothness filter, this image filter may refer to the observationthat volume scattering exhibits less speckle contrast compared todiffuse scattering materials. This image filter may quantify thesmoothness of the spot corresponding to speckle contrast as materialfeature. The material feature may be determined by

${\varphi_{2m} = {{\Phi\left( {f,z} \right)} = {\frac{\int{{❘{{(f)(x)} - {f(x)}}❘}{dx}}}{\int{{f(x)}dx}} \cdot \frac{1}{z}}}},$

wherein

is a smoothness function, for example a median filter or Gaussianfilter. This image filter may comprise dividing by the distance z, asdescribed in the formula above. The distance z may be determined forexample using a depth-from-defocus or depth-from-photon ratio techniqueand/or by using a triangulation technique. This may allow the filter tobe insensitive to distance. In one embodiment of the smoothness filter,the smoothness filter may be based on the standard deviation of anextracted speckle noise pattern. A speckle noise pattern N can bedescribed in an empirical way by

ƒ(x)=ƒ₀(x)·(N(X)+1),

where ƒ₀ is an image of a despeckled spot. N(X) is the noise term thatmodels the speckle pattern. The computation of a despeckled image may bedifficult. Thus, the despeckled image may be approximated with asmoothed version of f, i.e. ƒ₀≈

(ƒ), wherein

is a smoothness operator like a Gaussian filter or median filter. Thus,an approximation of the speckle pattern may be given by

${N(X)} = {\frac{f(x)}{\left( {f(x)} \right)} - {1.}}$

The material feature of this filter may be determined by

${\varphi_{2m} = {{\Phi(f)} = \sqrt{{Var}\left( {\frac{f}{(f)} - 1} \right)}}},$

Wherein Var denotes the variance function.

For example, the image filter may be a grey-level-occurrence-basedcontrast filter. This material filter may be based on the grey leveloccurrence matrix M_(ƒ,ρ)(g₁g₂)=[p_(g1,g2)], whereas p_(g1,g2) is theoccurrence rate of the grey combination (g₁,g₂)=[f(x₁,y₁),f(x₂,y₂)], andthe relation ρ defines the distance between (x₁,y₁) and (x₂,y₂), whichis ρ(x,y)=(x+a,y+b) with a and b selected from 0,1.

The material feature of the grey-level-occurrence-based contrast filtermay be given by

$\varphi_{2m} = {{\Phi(f)} = {\sum\limits_{i,{j = 0}}^{N - 1}{{p_{ij}\left( {i - j} \right)}^{2}.}}}$

For example, the image filter may be a grey-level-occurrence-basedenergy filter. This material filter is based on the grey leveloccurrence matrix defined above.

The material feature of the grey-level-occurrence-based energy filtermay be given by

$\varphi_{2m} = {{\Phi(f)} = {\sum\limits_{i,{j = 0}}^{N - 1}{\left( p_{ij} \right)^{2}.}}}$

For example, the image filter may be a grey-level-occurrence-basedhomogeneity filter. This material filter is based on the grey leveloccurrence matrix defined above.

The material feature of the grey-level-occurrence-based homogeneityfilter may be given by

$\varphi_{2m} = {{\Phi(f)} = {\sum\limits_{i,{j = 0}}^{N - 1}{\frac{p_{ij}}{1 + {❘{i - j}❘}}.}}}$

For example, the image filter may be a grey-level-occurrence-baseddissimilarity filter. This material filter is based on the grey leveloccurrence matrix defined above.

The material feature of the grey-level-occurrence-based dissimilarityfilter may be given by

$\varphi_{2m} = {{\Phi(f)} = {- {\sum\limits_{i,{j = 0}}^{N - 1}\sqrt{p_{ij}\log{\left( p_{ij} \right).}}}}}$

For example, the image filter may be a Law's energy filter. Thismaterial filter may be based on the laws vector L₅=[1,4,6,4,1] andE₅=[−1,−2,0,−2,−1] and the matrices L₅(E₅)^(T) and E₅(L₅)^(T). The imagef_(k) is convoluted with these matrices:

${f_{k,{L5E5}}^{\star}\left( {x,y} \right)} = {\sum\limits_{i - 2}^{2}{\sum\limits_{j - 2}^{2}{{f_{k}\left( {{x + i},{y + j}} \right)}{L_{5}\left( E_{5} \right)}^{T}}}}$and${f_{k,{L5E5}}^{\star}\left( {x,y} \right)} = {{\sum}_{i - 2}^{2}{\sum}_{j - 2}^{2}{f_{k}\left( {{x + i},{y + j}} \right)}{{E_{5}\left( L_{5} \right)}^{T}.}}$${E = {\int{\frac{f_{k,{L5E5}}^{*}\left( {x,y} \right)}{\max\left( {f_{k,{L5E5}}^{*}\left( {x,y} \right)} \right.}{dxdy}}}},$${F = {\int{\frac{f_{k,{L5E5}}^{*}\left( {x,y} \right)}{\max\left( {f_{k,{L5E5}}^{*}\left( {x,y} \right)} \right.}{dxdy}}}},$

Whereas the material feature of Law's energy filter may be determined by

φ_(2m)=φ(ƒ)=E/F.

For example, the material dependent image filter may be a threshold areafilter. This material feature may relate two areas in the image plane. Afirst area Ω1, may be an area wherein the function f is larger than αtimes the maximum of f. A second area Ω2, may be an area wherein thefunction f is smaller than a times the maximum of f, but larger than athreshold value c times the maximum of f. Preferably α may be 0.5 and εmay be 0.05. Due to speckles or noise, the areas may not simplycorrespond to an inner and an outer circle around the spot center. As anexample, Ω1 may comprise speckles or unconnected areas in the outercircle. The material feature may be determined by

${\varphi_{2m} = {{\Phi(f)} = \frac{\int_{\Omega 1}1}{\int_{\Omega 2}1}}},$

wherein Ω1={x|f(x)>α·max(f(x))} and Ω2={x|ε·max(f(x))<f(x)<α·max(f(x))}.

The evaluation device may be configured for using at least onepredetermined relationship between the material feature ϕ_(2m) and thematerial property of the surface having generated the reflection featurefor determining the material property of the surface having generatedthe reflection feature. The predetermined relationship may be one ormore of an empirical relationship, a semi-empiric relationship and ananalytically derived relationship. The evaluation device may comprise atleast one data storage device for storing the predeterminedrelationship, such as a lookup list or a lookup table.

Additionally or alternatively, the determining of the material propertyof the reflection features may be performed using artificialintelligence, in particular convolutional neuronal networks. Using thereflection image as input for convolutional neuronal networks may enablethe generation of classification models with sufficient accuracy todifferentiate between materials, e.g. between skin and othervolume-scattering materials. Since only physically valid information ispassed to the network by selecting important regions in the reflectionimage, only compact training data sets may be needed. Additionally, verycompact network architectures can be generated.

Specifically, at least one parametrized classification model may beused. The parametrized classification model may be configured forclassifying materials by using the reflection images as an input. Theclassification model may be parametrized by using one or more of machinelearning, deep learning, neural networks, or other form of artificialintelligence. The term “machine-learning” as used herein is a broad termand is to be given its ordinary and customary meaning to a person ofordinary skill in the art and is not to be limited to a special orcustomized meaning. The term specifically may refer, without limitation,to a method of using artificial intelligence (AI) for automaticallymodel building, in particular for parametrizing models. The term“classification model” may refer to a model configured fordiscriminating materials, e.g. human skin from other materials. Theproperty characteristic for the respective material may be determined byapplying an optimization algorithm in terms of at least one optimizationtarget on the classification model. The machine learning may be based onat least one neuronal network, in particular a convolutional neuralnetwork. Weights and/or topology of the neuronal network may bepre-determined and/or pre-defined. Specifically, the training of theclassification model may be performed using machine-learning. Theclassification model may comprise at least one machine-learningarchitecture and model parameters. For example, the machine-learningarchitecture may be or may comprise one or more of: linear regression,logistic regression, random forest, naive Bayes classifications, nearestneighbors, neural networks, convolutional neural networks, generativeadversarial networks, support vector machines, or gradient boostingalgorithms or the like. The term “training”, also denoted learning, asused herein, is a broad term and is to be given its ordinary andcustomary meaning to a person of ordinary skill in the art and is not tobe limited to a special or customized meaning. The term specifically mayrefer, without limitation, to a process of building the classificationmodel, in particular determining and/or updating parameters of theclassification model. The classification model may be at least partiallydata-driven. For example, the classification model may be based onexperimental data, such as data determined by illuminating a pluralityof humans and artificial objects such as masks and recording thereflection pattern. For example, the training may comprise using atleast one training dataset, wherein the training data set comprisesimages, in particular reflection images, e.g. of a plurality of humansand artificial objects with known material property.

In a further aspect, a detector for determining a position of at leastone object is disclosed. The detector comprises at least one projectorfor illuminating at least one object with at least one illuminationpattern comprising at least one illumination feature. The projectorcomprises at least one emitter is configured for generating at least onelight beam. The detector further comprises at least one camera having atleast one sensor element having a matrix of optical sensors, the opticalsensors each having a light-sensitive area. Each optical sensor isdesigned to generate at least one sensor signal in response to anillumination of its respective light-sensitive area by a reflectionlight beam propagating from the object to the camera. The camera isconfigured for imaging at least one reflection image comprising at leastone reflection feature generated by the object in response toillumination by the illumination feature. The reflection featurecomprises at least one beam profile. The detector further comprises atleast one evaluation device configured performing the following steps:

-   -   at least one image compression step, wherein the image        compression step comprises compressing the reflection image into        a compressed reflection image having a second bit depth lower        than the first bit depth, wherein the compression comprises        applying a non-linear grey value transformation on the sensor        signals;    -   at least one evaluation step, wherein the evaluation step        comprises evaluating the compressed reflection image, wherein        the evaluation comprises determining at least one longitudinal        coordinate for the reflection feature by analysis of its        respective beam profile.

The detector may be configured for performing the method according tothe present invention. such as according to one or more of theembodiments disclosed above or according to one or more of theembodiments disclosed in further detail below. For details, options anddefinitions, reference may be made to the method as discussed above.

In a further aspect, a mobile device configured for determining aposition of at least one object is disclosed. The mobile devicecomprises at least one detector according to the present invention suchas according to one or more of the embodiments disclosed above oraccording to one or more of the embodiments disclosed in further detailbelow. For details, options and definitions, reference may be made tothe detector and method as discussed above. The mobile device is one ormore of a mobile communication device such as a cell phone orsmartphone, a tablet computer, a portable computer.

Further disclosed and proposed herein is a computer program includingcomputer-executable instructions for performing the method according tothe present invention in one or more of the embodiments enclosed hereinwhen the program is executed on a computer or computer network.Specifically, the computer program may be stored on a computer-readabledata carrier and/or on a computer-readable storage medium.

As used herein, the terms “computer-readable data carrier” and“computer-readable storage medium” specifically may refer tonon-transitory data storage means, such as a hardware storage mediumhaving stored thereon computer-executable instructions. Thecomputer-readable data carrier or storage medium specifically may be ormay comprise a storage medium such as a random-access memory (RAM)and/or a read-only memory (ROM).

Thus, specifically, one, more than one or even all of method steps a) toc) as indicated above may be performed by using a computer or a computernetwork, preferably by using a computer program.

Further disclosed and proposed herein is a computer program producthaving program code means, in order to perform the method according tothe present invention in one or more of the embodiments enclosed hereinwhen the program is executed on a computer or computer network.Specifically, the program code means may be stored on acomputer-readable data carrier and/or on a computer-readable storagemedium.

Further disclosed and proposed herein is a data carrier having a datastructure stored thereon, which, after loading into a computer orcomputer network, such as into a working memory or main memory of thecomputer or computer network, may execute the method according to one ormore of the embodiments disclosed herein.

Further disclosed and proposed herein is a computer program product withprogram code means stored on a machine-readable carrier, in order toperform the method according to one or more of the embodiments disclosedherein, when the program is executed on a computer or computer network.As used herein, a computer program product refers to the program as atradable product. The product may generally exist in an arbitraryformat, such as in a paper format, or on a computer-readable datacarrier and/or on a computer-readable storage medium. Specifically, thecomputer program product may be distributed over a data network.

Finally, disclosed and proposed herein is a modulated data signal whichcontains instructions readable by a computer system or computer network,for performing the method according to one or more of the embodimentsdisclosed herein.

Referring to the computer-implemented aspects of the invention, one ormore of the method steps or even all of the method steps of the methodaccording to one or more of the embodiments disclosed herein may beperformed by using a computer or computer network. Thus, generally, anyof the method steps including provision and/or manipulation of data maybe performed by using a computer or computer network. Generally, thesemethod steps may include any of the method steps, typically except formethod steps requiring manual work, such as providing the samples and/orcertain aspects of performing the actual measurements.

Specifically, further disclosed herein are:

-   -   a computer or computer network comprising at least one        processor, wherein the processor is adapted to perform the        method according to one of the embodiments described in this        description,    -   a computer loadable data structure that is adapted to perform        the method according to one of the embodiments described in this        description while the data structure is being executed on a        computer,    -   a computer program, wherein the computer program is adapted to        perform the method according to one of the embodiments described        in this description while the program is being executed on a        computer,    -   a computer program comprising program means for performing the        method according to one of the embodiments described in this        description while the computer program is being executed on a        computer or on a computer network,    -   a computer program comprising program means according to the        preceding embodiment, wherein the program means are stored on a        storage medium readable to a computer,    -   a storage medium, wherein a data structure is stored on the        storage medium and wherein the data structure is adapted to        perform the method according to one of the embodiments described        in this description after having been loaded into a main and/or        working storage of a computer or of a computer network, and    -   a computer program product having program code means, wherein        the program code means can be stored or are stored on a storage        medium, for performing the method according to one of the        embodiments described in this description, if the program code        means are executed on a computer or on a computer network.

In a further aspect of the present invention, use of the detectoraccording to the present invention, such as according to one or more ofthe embodiments given above or given in further detail below, isproposed, for a purpose of use, selected from the group consisting of: aposition measurement in traffic technology; an entertainmentapplication; a security application; a surveillance application; asafety application; a human-machine interface application; a logisticsapplication; a tracking application; an outdoor application; a mobileapplication; a communication application; a photography application; amachine vision application; a robotics application; a quality controlapplication; a manufacturing application; a gait monitoring application;a human body monitoring application; home care; smart living, automotiveapplication.

With respect to further uses of the detector reference is made to WO2018/091649 A1, WO 2018/091638 A1 and WO 2018/091640 A1, the content ofwhich is included by reference.

As used herein, the terms “have”, “comprise” or “include” or anyarbitrary grammatical variations thereof are used in a non-exclusiveway. Thus, these terms may both refer to a situation in which, besidesthe feature introduced by these terms, no further features are presentin the entity described in this context and to a situation in which oneor more further features are present. As an example, the expressions “Ahas B”, “A comprises B” and “A includes B” may both refer to a situationin which, besides B, no other element is present in A (i.e. a situationin which A solely and exclusively consists of B) and to a situation inwhich, besides B, one or more further elements are present in entity A,such as element C, elements C and D or even further elements.

Further, it shall be noted that the terms “at least one”, “one or more”or similar expressions indicating that a feature or element may bepresent once or more than once typically are used only once whenintroducing the respective feature or element. In most cases, whenreferring to the respective feature or element, the expressions “atleast one” or “one or more” are not repeated, nonwithstanding the factthat the respective feature or element may be present once or more thanonce.

Further, as used herein, the terms “preferably”, “more preferably”,“particularly”, “more particularly”, “specifically”, “more specifically”or similar terms are used in conjunction with optional features, withoutrestricting alternative possibilities. Thus, features introduced bythese terms are optional features and are not intended to restrict thescope of the claims in any way. The invention may, as the skilled personwill recognize, be performed by using alternative features. Similarly,features introduced by “in an embodiment of the invention” or similarexpressions are intended to be optional features, without anyrestriction regarding alternative embodiments of the invention, withoutany restrictions regarding the scope of the invention and without anyrestriction regarding the possibility of combining the featuresintroduced in such way with other optional or non-optional features ofthe invention.

Summarizing and without excluding further possible embodiments, thefollowing embodiments may be envisaged:

-   -   Embodiment 1. A method for beam profile analysis using at least        one camera, wherein the camera has at least one sensor element        having a matrix of optical sensors, the optical sensors each        having a light-sensitive area, wherein each optical sensor is        designed to generate at least one sensor signal in response to        an illumination of its respective light-sensitive area by a        reflection light beam propagating from the object to the camera,        the method comprising the following steps:        -   a) at least one data acquisition step, wherein the data            acquisition step comprises illuminating at least one object            with at least one illumination pattern comprising at least            one illumination feature by using at least one projector,            wherein the projector comprises at least one emitter            configured for generating at least one light beam, wherein            the data acquisition step further comprises imaging, by            using the camera, at least one reflection image comprising            at least one reflection feature generated by the object in            response to illumination by the illumination feature,            wherein the reflection feature comprises at least one beam            profile, wherein the reflection image has a first bit depth;        -   b) at least one image compression step, wherein the image            compression step comprises compressing the reflection image            into a compressed reflection image having a second bit depth            lower than the first bit depth by using at least one            evaluation device, wherein the compression comprises            applying a non-linear grey value transformation on the            sensor signals;        -   c) at least one evaluation step, wherein the evaluation step            comprises evaluating the compressed reflection image by            using the evaluation device, wherein the evaluation            comprises determining at least one longitudinal coordinate            for the reflection feature by analysis of its respective            beam profile.    -   Embodiment 2. The method according to the preceding embodiment,        wherein the first bit depth is a number of bits per pixel of the        reflection image, and the second bit depth is a number of bits        per pixel of the compressed reflection image.    -   Embodiment 3. The method according to the preceding embodiment,        wherein the first bit depth is at least one bit depth selected        from the range consisting of 9 to 16, and the second bit depth        is 8 to 15 bit.    -   Embodiment 4. The method according to any one of the preceding        embodiments, wherein the non-linear grey value transformation        h(g) is applied using at least one pre-determined lookup table        of the non-linear grey value transformation h as a function of        the grey value g, wherein g is the grey value of a pixel with        the higher bit depth.    -   Embodiment 5. The method according to the preceding embodiment,        wherein the non-linear grey value transformation comprises        applying on the grey value g the non-linear grey value        transformation

${h(g)} = {h_{0} + {\frac{2\sigma_{g}}{\kappa}\left( {\sqrt{\sigma_{0}^{2} + {\kappa\left( {g - g_{off}} \right)}} - \sigma_{0}} \right)}}$

-   -   -   with σ_(g) being a standard deviation of the sensor signal,            K being a camera system gain, σ₀ ² being a dark noise,            g_(eff) being a pre-determined offset for g and h₀ being a            pre-determined offset for h.

    -   Embodiment 6. The method according to any one of the preceding        embodiments, wherein the analysis of the beam profile comprises        determining at least one first area and at least one second area        of the beam profile, wherein the analysis of the beam profile        further comprises deriving a combined signal Q by one or more of        dividing the first area and the second area, dividing multiples        of the first area and the second area, dividing linear        combinations of the first area and the second area, wherein the        analysis of the beam profile further comprises using at least        one predetermined relationship between the combined signal Q and        the longitudinal coordinate for determining the longitudinal        coordinate.

    -   Embodiment 7. The method according to any one of the preceding        embodiments, wherein the camera comprises at least one CCD        sensor or at least one CMOS sensor.

    -   Embodiment 8. The method according to any one of the preceding        embodiments, wherein the projector comprises at least one array        of emitters, wherein each of the emitters is configured for        generating at least one light beam.

    -   Embodiment 9. The method according to the preceding embodiment,        wherein each of the emitter is and/or comprises at least one        element selected from the group consisting of at least one laser        source such as at least one semi-conductor laser, at least one        double heterostructure laser, at least one external cavity        laser, at least one separate confinement heterostructure laser,        at least one quantum cascade laser, at least one distributed        Bragg reflector laser, at least one polariton laser, at least        one hybrid silicon laser, at least one extended cavity diode        laser, at least one quantum dot laser, at least one volume Bragg        grating laser, at least one Indium Arsenide laser, at least one        Gallium Arsenide laser, at least one transistor laser, at least        one diode pumped laser, at least one distributed feedback        lasers, at least one quantum well laser, at least one interband        cascade laser, at least one semiconductor ring laser, at least        one vertical cavity surface-emitting laser; at least one        non-laser light source such as at least one LED or at least one        light bulb.

    -   Embodiment 10. The method according to any one of the preceding        embodiments, wherein the light beam generated by the emitter has        a wavelength in a near infrared (NIR) regime, wherein the light        beam is generated by the emitter in an wavelength range from 800        to 1000 nm, preferably at about 940 nm.

    -   Embodiment 11. The method according to anyone of the preceding        method embodiments, wherein the method is computer-implemented.

    -   Embodiment 12. A detector for determining a position of at least        one object,        -   wherein the detector comprises at least one projector for            illuminating at least one object with at least one            illumination pattern comprising at least one illumination            feature, wherein the projector comprises at least one            emitter is configured for generating at least one light            beam;        -   wherein the detector further comprises at least one camera            having at least one sensor element having a matrix of            optical sensors, the optical sensors each having a            light-sensitive area, wherein each optical sensor is            designed to generate at least one sensor signal in response            to an illumination of its respective light-sensitive area by            a reflection light beam propagating from the object to the            camera, wherein the camera is configured for imaging at            least one reflection image comprising at least one            reflection feature generated by the object in response to            illumination by the illumination feature, wherein the            reflection feature comprises at least one beam profile;        -   wherein the detector further comprises at least one            evaluation device configured performing the following steps:            -   at least one image compression step, wherein the image                compression step comprises compressing the reflection                image into a compressed reflection image having a second                bit depth lower than the first bit depth, wherein the                compression comprises applying a non-linear grey value                transformation on the sensor signals;            -   at least one evaluation step, wherein the evaluation                step comprises evaluating the compressed reflection                image, wherein the evaluation comprises determining at                least one longitudinal coordinate for the reflection                feature by analysis of its respective beam profile.

    -   Embodiment 13. The detector according to the preceding        embodiment, wherein the detector is configured for performing        the method according to any one of the preceding embodiments        referring to a method.

    -   Embodiment 14. A mobile device comprising at least one detector        according to any one of the preceding embodiments referring to a        detector, wherein the mobile device is one or more of a mobile        communication device, a tablet computer, a portable computer.

    -   Embodiment 15. A computer program comprising instructions which,        when the program is executed by the detector according to any        one of the preceding embodiments referring to a detector, cause        the detector to perform the method according to any one of the        preceding embodiments referring to a method.

    -   Embodiment 16. A computer-readable storage medium comprising        instructions which, when the instructions are executed by the        detector according to any one of the preceding embodiments        referring to a detector, cause the detector to perform the        method according to any one of the preceding embodiments        referring to a method.

    -   Embodiment 17. A non-transient computer-readable medium        including instructions that, when executed by one or more        processors, cause the one or more processors to perform the        method according to any one of the preceding embodiments        relating to a method.

    -   Embodiment 18. A use of the detector according to any one of the        preceding embodiments referring to a detector, for a purpose of        use, selected from the group consisting of: a position        measurement in traffic technology; an entertainment application;        a security application; a surveillance application; a safety        application; a human-machine interface application; a logistics        application; a tracking application; an outdoor application; a        mobile application; a communication application; a photography        application; a machine vision application; a robotics        application; a quality control application; a manufacturing        application; a gait monitoring application; a human body        monitoring application; home care; smart living, automotive        application.

SHORT DESCRIPTION OF THE FIGURES

Further optional features and embodiments will be disclosed in moredetail in the subsequent description of embodiments, preferably inconjunction with the dependent claims.

Therein, the respective optional features may be realized in an isolatedfashion as well as in any arbitrary feasible combination, as the skilledperson will realize. The scope of the invention is not restricted by thepreferred embodiments. The embodiments are schematically depicted in theFigures. Therein, identical reference numbers in these Figures refer toidentical or functionally comparable elements.

In the Figures:

FIG. 1 shows a flowchart of an embodiment of a method for beam profileanalysis using at least one camera according to the present invention;

FIG. 2 shows an embodiment of a detector according to the presentinvention;

FIG. 3 shows experimental results, in particular a photon transfer curveof a camera; and

FIG. 4 shows an embodiment of a look up table.

DETAILED DESCRIPTION OF THE EMBODIMENTS

FIG. 1 shows a flowchart of an embodiment of a method for beam profileanalysis using at least one camera 110.

The method may be computer-implemented. The method may involving atleast one computer and/or at least one computer network. The computerand/or computer network may comprise at least one processor which isconfigured for performing at least one of the method steps of the methodaccording to the present invention. Specifically, each of the methodsteps is performed by the computer and/or computer network. The methodmay be performed completely automatically, specifically without userinteraction.

The beam profile analysis may comprise and/or may be a method fordetermining at least one physical quantity or property of an object 112such as a longitudinal coordinate and/or material property by using thebeam profile of a reflection light beam originating from the object 112.With respect to beam profile analysis reference is made to WO2018/091649 A1, WO 2018/091638 A1, WO 2018/091640 A1 and C. Lennartz, F.Schick, S. Metz, “Whitepaper—Beam Profile Analysis for 3D imaging andmaterial detection” Apr. 28, 2021, Ludwigshafen, Germany, the fullcontent of which is included by reference. The beam profile may be aspatial distribution, in particular in at least one plane perpendicularto the propagation of the light beam, of an intensity of the light beam.The beam profile may be a transverse intensity profile of the lightbeam. The beam profile may be a cross section of the light beam.

The beam profile may be selected from the group consisting of atrapezoid beam profile; a triangle beam profile; a conical beam profileand a linear combination of Gaussian beam profiles. Other embodimentsare feasible, however.

The object 112 may be an arbitrary object 112 to be measured, inparticular a surface or region, which is configured to reflect at leastpartially at least one light beam impinging on the object 112. Forexample, the object 112 may be a human being, in particular skin. Theobject 112 may comprise a surface or region, which is configured for atleast partially one or more of reflecting and/or scattering and/oremitting in response to at least one light beam impinging on the object112. The object 112 may be part of a scene comprising the object 112 andfurther surrounding environment.

The camera 110 may be an element of a detector 114. An embodiment of thedetector is shown in FIG. 2 . The detector 114 may be an arbitrarysensor device configured for determining and/or detecting and/or sensingthe object. The detector 114 may be a stationary device or a mobiledevice. Further, the detector 114 may be a stand-alone device or mayform part of another device, such as a computer, a vehicle or any otherdevice. Further, the detector 114 may be a hand-held device. Otherembodiments of the detector are feasible. The detector 114 may be one ofattached to or integrated into a mobile device 116 such as a mobilephone or smartphone. The detector 114 may be integrated in a mobiledevice 116, e.g. within a housing of the mobile device. Additionally oralternatively, the detector 114, or at least one component of thedetector, may be attached to the mobile device 116 such as by using aconnector such as a USB or phone-connector such as the headphone jack.

The camera 110 has at least one sensor element 118 having a matrix ofoptical sensors. The optical sensors each having a light-sensitive area.Each optical sensor is designed to generate at least one sensor signalin response to an illumination of its respective light-sensitive area bya reflection light beam propagating from the object 112 to the camera110.

As shown in FIG. 1 , the method comprises the following steps:

-   -   a) (120) at least one data acquisition step, wherein the data        acquisition step comprises illuminating the at least one object        112 with at least one illumination pattern 122 comprising at        least one illumination feature by using at least one projector        124, wherein the projector 124 comprises at least one emitter        126 configured for generating at least one light beam, wherein        the data acquisition step further comprises imaging, by using        the camera 110, at least one reflection image comprising at        least one reflection feature generated by the object 112 in        response to illumination by the illumination feature, wherein        the reflection feature comprises at least one beam profile,        wherein the reflection image has a first bit depth;    -   b) (128) at least one image compression step, wherein the image        compression step comprises compressing the reflection image into        a compressed reflection image having a second bit depth lower        than the first bit depth by using at least one evaluation device        130, wherein the compression comprises applying a non-linear        grey value transformation on the sensor signals;    -   c) (132) at least one evaluation step, wherein the evaluation        step comprises evaluating the compressed reflection image by        using the evaluation device 130, wherein the evaluation        comprises determining at least one longitudinal coordinate for        the reflection feature by analysis of its respective beam        profile.

The projector 124, as shown in FIG. 2 , may be an optical deviceconfigured to project at least one illumination pattern onto the object,specifically onto a surface of the object. The projector 124 isconfigured for illuminating the object 112 with the at least oneillumination pattern 122 comprising a plurality of illuminationfeatures. The illumination feature may be at least one at leastpartially extended feature of the illumination pattern 122. Theillumination pattern 122 may comprise at least one pattern selected fromthe group consisting of: at least one quasi random pattern; at least oneSobol pattern; at least one quasiperiodic pattern; at least one pointpattern, in particular a pseudo-random point pattern; at least one linepattern; at least one stripe pattern; at least one checkerboard pattern;at least one triangular pattern; at least one rectangular pattern; atleast one hexagonal pattern or a pattern comprising further convextilings. The illumination pattern 122 may exhibit the at least oneillumination feature selected from the group consisting of: at least onepoint; at least one line; at least two lines such as parallel orcrossing lines; at least one point and one line; at least onearrangement of periodic features; at least one arbitrary shaped featuredpattern. For example, the illumination pattern 122 comprises at leastone pattern comprising at least one pre-known feature. For example, theillumination pattern 122 comprises at least one line pattern comprisingat least one line. For example, the illumination pattern 122 comprisesat least one line pattern comprising at least two lines such as parallelor crossing lines. For example, the projector 124 may be configured forgenerate and/or to project a cloud of points or non-point-like features.For example, the projector 124 may be configured for generate a cloud ofpoints or non-point-like features such that the illumination pattern 122may comprise a plurality of point features or non-point-like features.For example, the illumination pattern 122 comprises at least 10, 20,100, 1000 or even more illumination features.

The projector 124 comprises the at least one emitter 126 configured forgenerating at least one light beam. The projector 124 may comprise atleast one array of emitters 126. Each of the emitters 126 may beconfigured for emitting at least one light beam. The emitter 126 may beat least one arbitrary device configured for providing the at least onelight beam for illumination of the object 112. Each of the emitters 126may be and/or may comprise at least one element selected from the groupconsisting of at least one laser source such as at least onesemi-conductor laser, at least one double heterostructure laser, atleast one external cavity laser, at least one separate confinementheterostructure laser, at least one quantum cascade laser, at least onedistributed Bragg reflector laser, at least one polariton laser, atleast one hybrid silicon laser, at least one extended cavity diodelaser, at least one quantum dot laser, at least one volume Bragg gratinglaser, at least one Indium Arsenide laser, at least one Gallium Arsenidelaser, at least one transistor laser, at least one diode pumped laser,at least one distributed feedback lasers, at least one quantum welllaser, at least one interband cascade laser, at least one semiconductorring laser, at least one vertical cavity surface-emitting laser (VCSEL);at least one non-laser light source such as at least one LED or at leastone light bulb. For example, the emitters 126 may be an array of VCSELs.Each of the VCSELs is configured for generating at least one light beam.The VCSELs may be arranged on a common substrate or on differentsubstrates. The array may comprise up to 2500 VCSELs. For example, thearray may comprise 38×25 VCSELs, such as a high power array with 3.5 W.For example, the array may comprise 10×27 VCSELs with 2.5 W. Forexample, the array may comprise 96 VCSELs with 0.9 W. A size of thearray, e.g. of 2500 elements, may be up to 2 mm×2 mm.

The light beam emitted by the respective emitter 126 may have awavelength of 300 to 1100 nm, preferably 500 to 1100 nm. For example,the light beam may have a wavelength of 940 nm. For example, light inthe infrared spectral range may be used, such as in the range of 780 nmto 3.0 μm. Specifically, the light in the part of the near infraredregion where silicon photodiodes are applicable specifically in therange of 700 nm to 1100 nm may be used. The emitter 126 may beconfigured for generating the at least one illumination pattern in theinfrared region, in particular in the near infrared region. Using lightin the near infrared region may allow that light is not or only weaklydetected by human eyes and is still detectable by silicon sensors, inparticular standard silicon sensors. For example, the emitters 126 maybe an array of VCSELs. The VCSELs may be configured for emitting lightbeams at a wavelength range from 800 to 1000 nm. For example, the VCSELsmay be configured for emitting light beams at 808 nm, 850 nm, 940 nm, or980 nm. Preferably the VCSELs emit light at 940 nm, since terrestrialsun radiation has a local minimum in irradiance at this wavelength, e.g.as described in CIE 085-1989 “Solar spectral Irradiance”.

The projector 124 may comprise at least one transfer device, not shownhere, configured for generating the illumination features from the lightbeams impinging on the transfer device.

The data acquisition step 120 further comprises imaging, by using thecamera 110, at least one reflection image comprising at least onereflection feature generated by the object 112 in response toillumination by the illumination feature. The camera 110 may be a devicehaving at least one imaging element configured for recording orcapturing spatially resolved one-dimensional, two-dimensional or eventhree-dimensional optical data or information. The camera 110 may be adigital camera. As an example, the camera 110 may comprise at least onecamera chip, such as at least one CCD chip and/or at least one CMOS chipconfigured for recording images. The camera 110 may be or may compriseat least one near infrared camera. The camera 110, besides the at leastone camera chip or imaging chip, may comprise further elements, such asone or more optical elements, e.g. one or more lenses. As an example,the camera 110 may be a fix-focus camera, having at least one lens whichis fixedly adjusted with respect to the camera. Alternatively, however,the camera 110 may also comprise one or more variable lenses which maybe adjusted, automatically or manually.

The camera 110 may be a camera of a mobile device such as of notebookcomputers, tablets or, specifically, cell phones such as smart phonesand the like. Thus, specifically, the camera 110 may be part of themobile device 116 which, besides the camera 110, comprises one or moredata processing devices such as one or more data processors. Othercameras, however, are feasible.

The optical sensors of the camera 110 may be sensitive in one or more ofthe ultraviolet, the visible or the infrared spectral range.Specifically, the optical sensors may be sensitive in the visiblespectral range from 500 nm to 780 nm, most preferably at 650 nm to 750nm or at 690 nm to 700 nm. Specifically, the optical sensors may besensitive in the near infrared region. Specifically, the optical sensorsmay be sensitive in the part of the near infrared region where siliconphotodiodes are applicable specifically in the range of 700 nm to 1000nm. The optical sensors, specifically, may be sensitive in the infraredspectral range, specifically in the range of 780 nm to 3.0 micrometers.For example, the optical sensors each, independently, may be or maycomprise at least one element selected from the group consisting of aphotodiode, a photocell, a photoconductor, a phototransistor or anycombination thereof. For example, the optical sensors may be or maycomprise at least one element selected from the group consisting of aCCD sensor element, a CMOS sensor element, a photodiode, a photocell, aphotoconductor, a phototransistor or any combination thereof. Any othertype of photosensitive element may be used. The photosensitive elementgenerally may fully or partially be made of inorganic materials and/ormay fully or partially be made of organic materials. Most commonly, oneor more photodiodes may be used, such as commercially availablephotodiodes, e.g. inorganic semiconductor photodiodes.

The camera 110 may comprise a global shutter, not shown here, and/or maybe operated in a global shutter modus. The global shutter may be anelectronic shutter configured for quantizing exposure time of the sensorelement. The global shutter may be configured such that exposure of eachpixel of the sensor element starts and ends at the same time.

The camera 110 may comprise a transfer device, not shown here,configured for guiding the light beam onto the optical sensors and forforming the reflection image on the sensor element 118. The detector 114may comprise an optical axis. For example, the transfer device mayconstitute a coordinate system, wherein a longitudinal coordinate z is acoordinate along an optical axis of the transfer device. The coordinatesystem may be a polar coordinate system in which the optical axis of thetransfer device forms a z-axis and in which a distance from the z-axisand a polar angle may be used as additional coordinates. For example,the transfer device may constitute a coordinate system in which theoptical axis of the detector forms the z-axis and in which,additionally, an x-axis and a y-axis may be provided which areperpendicular to the z-axis and which are perpendicular to each other.An exemplary coordinate system is shown in FIG. 2 .

The reflection image may be an image determined by the camera 110comprising a plurality of reflection features. The reflection featuremay be a feature in an image plane generated by the object 112 inresponse to illumination with at least one illumination feature. Thereflection image may comprise the at least one reflection patterncomprising the reflection features. The imaging at least one refectionimage may comprise one or more of capturing, recording and generating ofthe reflection image. The reflection feature comprises at least one beamprofile. Each of the reflection features comprises at least one beamprofile.

The reflection image has a first bit depth. The bit depth defines anumber of bits per pixel in the image. The higher the first bit depth,the more information can be stored. For example, the first bit depth isat least one bit depth selected from the range consisting of 9 to 16.For example, the first bit depth may be 10 bit. However, even higherfirst bit depths may be possible, e.g. 32 bit.

For example, for storage and/or transmission and/or further analysis thefirst bit depth may need to be compressed. The compression may comprisetransforming information using fewer bits than the originalrepresentation. The compression may comprise quantization by compressinga range of values to a single quantum value. The quantization may beperformed using a defined number of quantization levels.

As outlined above, the method comprises, in step b) 128, at least oneimage compression step. The image compression step 128 comprisescompressing the reflection image into a compressed reflection imagehaving a second bit depth lower than the first bit depth by using atleast one evaluation device. The second bit depth may be a number ofbits per pixel of the compressed reflection image. The second bit depthis lower than the first bit depth. For example, the second bit depth is8 to 15 bit.

Usually, compressions are lossy, in particular may result into loss insignal range, resolution, and/or contrast. For reliable results usingbeam profile analysis, however, the signal may need to be as “physical”as possible. Further known techniques use equidistant quantization forimage sensors. However, in case of a linear camera having low darknoise, equidistant quantization may not be suitable. The camera 110 mayhave low dark noise. Therefore, a variance of a temporal noise mayincrease linearly with the sensor signal. In order to be able to resolvea low noise in a dark part of the picture, many quantization levelswould be required. As a result, however, in the bright part, thequantization may become much too fine, so that the standard deviation ofthe temporal noise much exceeds that of the quantization levels. Thismeans that the usual equidistant quantization for image sensors may besuboptimal.

The compression comprises applying a non-linear grey valuetransformation on the sensor signals. The non-linear grey valuetransformation may be configured as described in Jähne, B. andSchwarzbauer, M., “Noise equalisation and quasi loss-less image datacompression—or how many bits needs an image sensor?” tm—TechnischesMessen, 83, 16-24, doi: 10.1515/teme-2015-0093, published online 17 Dec.2015, 2016. The non-linear grey value transformation may allow imagecompressing from the first bit depth, e.g. 10 bit, to the lower secondbit depth, e.g. 8 bit, and at the same time to minimize loss ofinformation, in particular in signal range, resolution, and/or contrast.

Without to be bound by this theory, the variance σ² _(g)(g) of thetemporal noise may strongly depend on the grey value g. A relationshipbetween variance and a mean value of the grey value may be described bythe so-called photon transfer curve.

By means of non-linear grey value transformation the temporal noise canbe modified in such a way that the standard deviation of the temporalnoise becomes independent of the grey value, resulting in anoise-equilibrated signal. As described in Jähne, B.: DigitaleBildverarbeitung, Springer, Berlin, 6 edn., doi: 10.1007/b138991, 2005,it is known from laws governing the propagation of errors, that thevariance of a non-linear function h(g) results, in the first order, to

$\begin{matrix}{\sigma_{0}^{2} \approx {\left( \frac{dh}{dg} \right)^{2}{{\sigma_{g}^{2}(g)}.}}} & (1)\end{matrix}$

If σ² _(h) is to a constant value, formula (1) can be transformed to

${dh} = {\frac{\sigma_{h}}{\sqrt{\sigma^{2}}}{{dg}.}}$

and integration results in

$\begin{matrix}{{h(g)} = {\sigma_{h}{\int{\frac{{dg}^{\prime}}{\sqrt{\sigma^{2}\left( g^{\prime} \right)}}.}}}} & (2)\end{matrix}$

The integration constant may be chosen so that h(0)=0. Equation (2)expresses that there is an analytical solution for each function σ²_(g)(g) for which the integral can be solved. Otherwise, it may bepossible to integrate numerically.

With a linear camera, the variance of the temporal noise increaseslinearly with the grey value g:

σ² _(g)(g)=σ₀ ²+Kg  (3)

Here the variance of the dark noise is σ₀ ² in DN and K is theamplification of the camera in DN/electron, see EMVA Standard1288—Standard for Characterization of Image Sensors and Cameras, Release3.1, open standard, European Machine Vision Association, doi:10.5281/zenodo. 235942, 2016. With the linear variance function (3) theintegral in (2) calculated to be

$\begin{matrix}{{h(g)} = {\frac{2\sigma_{h}}{K}{\left( {\sqrt{\sigma_{0}^{2} + {Kg}} - \sigma_{0}} \right).}}} & (4)\end{matrix}$

The free parameter σ_(h) and the constant standard deviation of thetemporal noise in the non-linearly transformed signal h(g) can be usedin order to determine the signal range [0, h_(max)], and/or a necessarynumber of bits for the compressed signal. With

σ_(max) ²=σ² _(g)(g _(max))  (5)

equation (3) can be rewritten as follows

σ_(g) ²(g)=σ₀ ²+(σ_(max) ²−σ₀ ²)g/g _(max).  (6)

Wherein g_(max) is a maximum grey value. This results in the conditionh(g_(max))=h_(max) and

$\begin{matrix}{\frac{h}{h_{\max}} = {{\frac{\sqrt{\sigma_{0}^{2} + {\left( {\sigma_{\max}^{2} - \sigma_{0}^{2}} \right){g/g_{\max}}} - \sigma_{0}}}{\sigma_{\max} - \sigma_{0}}{with}{}\sigma_{h}} = {\frac{h_{\max}}{2} \cdot {\frac{\sigma_{\max} + \sigma_{0}}{g_{\max}}.}}}} & (7)\end{matrix}$

Thus, it may be possible to calculate how many bits are necessary forsuitable quantization of a noise-equilibrated signal:

$\begin{matrix} & (8)\end{matrix}$$h_{\max} = {{2{\sigma_{h} \cdot \frac{g_{\max}}{\sigma_{\max} + \sigma_{0}}}} = {{2{\sigma_{h} \cdot {{SNR}_{\max}/\left( {1 + \frac{\sigma_{0}}{\sigma_{\max}}} \right)}}} \approx {2{\sigma_{h} \cdot {{SNR}_{\max}.}}}}}$

The approximation on the right-hand side can be used becauseσ_(max)>>σ₀. Therefore, the maximum (signal to noise ratio) SNR of thecamera may determine how many bits are required for a sufficientquantization independently of the dark noise. From this equation it canbe derived how many bits are required to quantize the equalized signalh. This is given by the value of h_(max). σ_(h) may be between 0.5and 1. This may allow an optimum quantization, see Jähne, B. andSchwarzbauer, M., “Noise equalisation and quasi loss-less image datacompression—or how many bits needs an image sensor?” tm—TechnischesMessen, 83, 16-24, doi: 10.1515/teme-2015-0093, published online 17 Dec.2015, 2016.

These considerations show that it is possible to use a non-linear greyvalue transformation for compression of image data of a camera, inparticular of a linear camera with a maximum SNR of <126 and aquantization of 8 bit with σ_(h)=1 by means of a non-linear grey valuetransformation. In this way, the entire signal range of the camera canbe covered.

Table 1 comprises camera characteristics for five different cameras 110:

TABLE 1 Allied Vision Allied Vision Manufacturer OMNIVISION ™ TISTechnologies ™ Technologies ™ Basler ™ a2A Camera OV9282 37BUX2901800U-240 1800U-501 3840-45 um Sensor OV9282-H64A IMX290LLR IMX392AR0522 IMX334 Shutter global rolling rolling global rolling Bit depth 1010 12 10 12 Fundamental properties Pixel size (μm) 3.0 2.9 3.45 2.2 2.0Resolution 1280 × 800 1920 × 1080 1 1936 × 1216 2592 × 1944 3840 × 21601.0 Mpixel 2.1 Mpixel 2.4 Mpixel 5.0 Mpixel 8.3 Mpixel Image rate 8 120143 126 67 45 bit (fps) EMVA 1288 data measured or according to themanufacturer QE (529 nm) — — 0.64 0.84 0.73 QE (938 nm) 0.085 — 0.060.20 — Dark noise (e⁻) 3.3 — 2.1 6.9 2.0 Saturation cap. (e⁻) 4 600 —10400 10600 7600 Dynamic range 1 100 — 5000 1540 3800 SNR_(max) 68 — 102103 87 Sat./area (e⁻/μm²) 500 — 870 2100 1900 LUT calculation σ₀ (DN)0.67 — — — — K (DN/e⁻) 0.185 — — — — g₀ 3 — — — — h₀ 8 — — — — Exposureintensity range (optics with a specified f-number) Dark noise (p/μm²)4.3 — 2.9 7.1 — Saturation cap. (p/μm²) 5 900 — 14600 11000 — SNR at5000 p/μm² 63 — 60 69 — dto. 3.0 μm 63 — 52 94 —

This table shows none of the cameras 110 has an SNR in excess of 103.Therefore all cameras 110 can be operated within their entire dynamicrange with 8 bits. If these cameras 110 would operated with linearcharacteristics and 8 bits, the dynamics of the sensors would by nomeans be fully deployed, but are restricted by the quantization. E.g.for the OV9282, if one assumes a signal handling of optimally 256 with 8bits linear, this would be about 5.3 times worse than the signal rangeof the camera itself. For the other cameras 110 having an even greatersignal range, the benefit of using the non-linear grey valuetransformation would be considerable higher. For example. In the case ofthe IMX392, the signal range can be increased by the non-lineartransformation by nearly 20 times. Thus, in all cases using thenon-linear grey value transformation can result in a considerableimprovement in the signal range, which for the determination of thelongitudinal coordinate is reflected in an extended depth range.

The non-linear grey value transformation h(g) may be applied using atleast one pre-determined lookup table of the non-linear grey valuetransformation h as a function of the grey value g, wherein g is thegrey value of a pixel with the higher bit depth. For example, in case ofcompression to 8 bit, depending on the resolution of the camera, thelookup table may have 2¹⁰ or 2¹² values with 8 bit resolution.

For example, the non-linear grey value transformation may compriseapplying on the grey value g the non-linear grey value transformation asdescribed in equation (4). Using this formula may be advantageousbecause the required parameters can be gained directly from EMVAStandard 1288—(Standard for Characterization of Image Sensors andCameras, Release 3.1, open standard, European Machine VisionAssociation, doi: 10.5281/zenodo. 235942, 2016) measurements, the darknoise σ₀ in DN (in the EMVA 1288 Standard it is called σ_(y,dark)) andthe amplification K. Both parameters result from the offset and thegradient of the photon transfer curve. For the photon transfer curve noabsolute radiometric measurement may be necessary, because the varianceof the temporal noise is plotted as a function of the photo-induced meangrey value. FIG. 3 shows an exemplary photon transfer curve of anOMNIVISION™ OV9282 camera.

In particular, the non-linear grey value transformation may compriseapplying on the grey value g the non-linear grey value transformation

${h(g)} = {h_{0} + {\frac{2\sigma_{g}}{\kappa}\left( {\sqrt{\sigma_{0}^{2} + {\kappa\left( {g - g_{off}} \right)}} - \sigma_{0}} \right)}}$

with σ_(g) being a standard deviation of the sensor signal, K being acamera system gain, σ₀ ² being a dark noise, g_(off) being apre-determined offset for g and h, being a pre-determined offset for h.Using this formula may ensure the correct choice of the offsets for Band h, since on account of the temporal noise, values might be obtainedwhich fall below the mean dark value. g₀ may be chosen in such a waythat, first of all, the mean dark value a μ_(g,dark) of the camera istaken into account and then an additional value g₀ is subtracted, whichresults in g_(offs)=μ_(g,dark)+g₀. FIG. 4 shows an exemplary lookuptable for the non-linear transformation with compression from 10 bit to8 bit, fully maintaining the signal dynamics, for the OMNIVISION™ OV9282camera. with σ_(h)=1 and the measured values for σ₀=0.67, K=0.185, h₀=8and g₀=3 (see table 1). From the stepped shape of the lookup table itcan be seen that at high grey values g many values fall on the samevalue h, because there the temporal noise is much greater than thequantization. At smaller grey values, the steps become increasinglynarrower as the temporal noise decreases. The lookup table is similarbut not identical to a square root function or a gamma value of 0.5.

The detector 114 further comprises the evaluation device 130 configuredfor determining at least one longitudinal coordinate for the reflectionfeatures by analysis of its respective beam profile.

The evaluation device 130 may be configured for selecting reflectionfeatures of the reflection image. The selecting may comprise to one ormore of identifying, determining and choosing at least one reflectionfeature of the reflection image. The evaluation device 130 may beconfigured for performing at least one image analysis and/or imageprocessing in order to identify the reflection features. The imageanalysis and/or image processing may use at least one feature detectionalgorithm. The image analysis and/or image processing may comprise oneor more of the following: a filtering; a selection of at least oneregion of interest; a formation of a difference image between an imagecreated by the sensor signals and at least one offset; an inversion ofsensor signals by inverting an image created by the sensor signals; aformation of a difference image between an image created by the sensorsignals at different times; a background correction; a decompositioninto color channels; a decomposition into hue; saturation; andbrightness channels; a frequency decomposition; a singular valuedecomposition; applying a Canny edge detector; applying a Laplacian ofGaussian filter; applying a Difference of Gaussian filter; applying aSobel operator; applying a Laplace operator; applying a Scharr operator;applying a Prewitt operator; applying a Roberts operator; applying aKirsch operator; applying a high-pass filter; applying a low-passfilter; applying a Fourier transformation; applying aRadon-transformation; applying a Hough-transformation; applying awavelet-transformation; a thresholding; creating a binary image. Theregion of interest may be determined manually by a user or may bedetermined automatically, such as by recognizing an object within animage generated by the optical sensors.

The evaluation device 130 is configured for determining at least onelongitudinal coordinate, also denoted as z_(DPR), for the reflectionfeatures by analysis of their beam profiles. The analysis of the beamprofile may comprise evaluating of the beam profile and may comprise atleast one mathematical operation and/or at least one comparison and/orat least symmetrizing and/or at least one filtering and/or at least onenormalizing. For example, the analysis of the beam profile may compriseat least one of a histogram analysis step, a calculation of a differencemeasure, application of a neural network, application of a machinelearning algorithm. The evaluation device 130 may be configured forsymmetrizing and/or for normalizing and/or for filtering the beamprofile, in particular to remove noise or asymmetries from recordingunder larger angles, recording edges or the like. The evaluation device130 may filter the beam profile by removing high spatial frequenciessuch as by spatial frequency analysis and/or median filtering or thelike. Summarization may be performed by center of intensity of the lightspot and averaging all intensities at the same distance to the center.The evaluation device 130 may be configured for normalizing the beamprofile to a maximum intensity, in particular to account for intensitydifferences due to the recorded distance. The evaluation device 130 maybe configured for removing influences from background light from thebeam profile, for example, by an imaging without illumination.

The reflection feature may cover or may extend over at least one pixelof the image. For example, the reflection feature may cover or mayextend over plurality of pixels. The evaluation device 130 may beconfigured for determining and/or for selecting all pixels connected toand/or belonging to the reflection feature, e.g. a light spot. Theevaluation device 130 may be configured for determining the center ofintensity by

${R_{coi} = \frac{1}{l \cdot {\sum{j \cdot r_{pixel}}}}},$

wherein R_(coi) is a position of center of intensity, r_(pixel) is thepixel position and l=Σ_(j)I_(total) with j being the number of pixels jconnected to and/or belonging to the reflection feature and I_(total)being the total intensity.

The evaluation device 130 may be configured for determining thelongitudinal coordinate for each of the reflection features by usingdepth-from-photon-ratio technique, also denoted as beam profileanalysis. With respect to depth-from-photon-ratio (DPR) techniquereference is made to WO 2018/091649 A1, WO 2018/091638 A1, WO2018/091640 A1 and C. Lennartz, F. Schick, S. Metz, “Whitepaper—BeamProfile Analysis for 3D imaging and material detection” Apr. 28, 2021,Ludwigshafen, Germany, the full content of which is included byreference.

The evaluation device 130 may further be configured for determining ofthe material property of the object by using beam profile analysis.Specifically, beam profile analysis makes use of reflection propertiesof coherent light projected onto object surfaces to classify materials.The determining of the material property may be performed as describedin one of WO 2020/187719, WO 2021/170791 A1 and/or WO 2021/152070, thefull content of which is included by reference. Specifically, analyzingof the beam profile of the reflection feature may be performed byfeature-based methods. Additionally or alternatively, machine learningmethods may be used. The feature based methods may be used incombination with machine learning methods which may allowparametrization of a classification model. Alternatively or incombination, convolutional neuronal networks may be utilized to classifymaterials by using the reflection image as an input.

LIST OF REFERENCE NUMBERS

-   110 camera-   112 object-   114 detector-   116 mobile device-   118 sensor element-   120 step a)-   122 illumination pattern-   124 projector-   126 emitter-   128 step b)-   130 evaluation device-   132 step c)

1-15. (canceled)
 16. A method for beam profile analysis using at leastone camera, wherein the camera has at least one sensor element having amatrix of optical sensors, the optical sensors each having alight-sensitive area, wherein each optical sensor is designed togenerate at least one sensor signal in response to an illumination ofits respective light-sensitive area by a reflection light beampropagating from the object to the camera, the method comprising thefollowing steps: a) at least one data acquisition step, wherein the dataacquisition step comprises illuminating at least one object with atleast one illumination pattern comprising at least one illuminationfeature by using at least one projector, wherein the projector comprisesat least one emitter configured for generating at least one light beam,wherein the data acquisition step further comprises imaging, by usingthe camera, at least one reflection image comprising at least onereflection feature generated by the object in response to illuminationby the illumination feature, wherein the reflection feature comprises atleast one beam profile, wherein the reflection image has a first bitdepth; b) at least one image compression step, wherein the imagecompression step comprises compressing the reflection image into acompressed reflection image having a second bit depth lower than thefirst bit depth by using at least one evaluation device, wherein thecompression comprises applying a non-linear grey value transformation onthe sensor signals; and c) at least one evaluation step, wherein theevaluation step comprises evaluating the compressed reflection image byusing the evaluation device, wherein the evaluation comprisesdetermining at least one longitudinal coordinate for the reflectionfeature by analysis of its respective beam profile.
 17. The methodaccording to claim 16, wherein the first bit depth is a number of bitsper pixel of the reflection image, and the second bit depth is a numberof bits per pixel of the compressed reflection image.
 18. The methodaccording to claim 17, wherein the first bit depth is at least one bitdepth selected from the range consisting of 9 to 16, and the second bitdepth is 8 to 15 bit.
 19. The method according to claim 16, wherein thenon-linear grey value transformation h(g) is applied using at least onepre-determined lookup table of the non-linear grey value transformationh as a function of the grey value g, wherein g is the grey value of apixel with the higher bit depth.
 20. The method according to claim 19,wherein the non-linear grey value transformation comprises applying onthe grey value g the non-linear grey value transformation${h(g)} = {h_{0} + {\frac{2\sigma_{g}}{\kappa}\left( {\sqrt{\sigma_{0}^{2} + {\kappa\left( {g - g_{off}} \right)}} - \sigma_{0}} \right)}}$with σ_(g) being a standard deviation of the sensor signal, K being acamera system gain, σ₀ ² being a dark noise, g_(off) being apre-determined offset for g and h₀ being a pre-determined offset for h.21. The method according to claim 16, wherein the analysis of the beamprofile comprises determining at least one first area and at least onesecond area of the beam profile, wherein the analysis of the beamprofile further comprises deriving a combined signal Q by one or more ofdividing the first area and the second area, dividing multiples of thefirst area and the second area, dividing linear combinations of thefirst area and the second area, wherein the analysis of the beam profilefurther comprises using at least one predetermined relationship betweenthe combined signal Q and the longitudinal coordinate for determiningthe longitudinal coordinate.
 22. The method according to claim 21,wherein the first area of the beam profile comprises essentially edgeinformation of the beam profile and the second area of the beam profilecomprises essentially center information of the beam profile.
 23. Themethod according to claim 16, wherein the camera comprises at least oneCCD sensor or at least one CMOS sensor.
 24. The method according toclaim 16, wherein the camera is sensitive in the wavelength range of 780nm to 3.0 micrometers.
 25. The method according to claim 16, wherein thecamera comprises a global shutter, wherein the global shutter isconfigured such that exposure of each pixel of the sensor element startsand ends at the same time.
 26. The method according to claim 16, whereinthe projector comprises at least one array of emitters, wherein each ofthe emitters is configured for generating at least one light beam. 27.The method according to claim 26, wherein the projector comprises avertical cavity surface-emitting laser (VCSEL).
 28. The method accordingto claim 16, wherein the light beam generated by the emitter has awavelength in a near infrared (NIR) regime, wherein the light beam isgenerated by the emitter in a wavelength range from 800 to 1000 nm. 29.The method according to claim 28, wherein the light beam generated bythe emitter has a wavelength of about 940 nm.
 30. The method accordingto claim 16, wherein the illumination pattern comprises an arrangementof periodic features.
 31. The method according to claim 30, wherein theillumination pattern comprises 1000 or more illumination features. 32.The method according to claim 30, wherein the illumination patterncomprises a triangular pattern, a rectangular pattern, hexagonal patternor a pattern comprising further convex tilings.
 33. The method accordingto claim 16, wherein the projector comprises a diffractive opticalelement (DOE) configured for generating the illumination pattern. 34.The method according to claim 16, wherein the evaluation step mayfurther comprise determining at least one material property of theobject by analysis of the beam profile of the reflection feature. 35.The method according to claim 34, wherein determining at least onematerial property involves a convolutional neuronal network configuredto classify materials by using the reflection image as an input.
 36. Themethod according to claim 34, wherein determining at least one materialproperty comprises determining if a reflection feature has beengenerated by reflection from human skin.
 37. The method according toclaim 16, wherein the method is computer-implemented.
 38. A detector fordetermining a position of at least one object, wherein the detectorcomprises at least one projector for illuminating at least one objectwith at least one illumination pattern comprising at least oneillumination feature, wherein the projector comprises at least oneemitter configured for generating at least one light beam; wherein thedetector further comprises at least one camera having at least onesensor element having a matrix of optical sensors, the optical sensorseach having a light-sensitive area, wherein each optical sensor isdesigned to generate at least one sensor signal in response to anillumination of its respective light-sensitive area by a reflectionlight beam propagating from the object to the camera, wherein the camerais configured for imaging at least one reflection image comprising atleast one reflection feature generated by the object in response toillumination by the illumination feature, wherein the reflection featurecomprises at least one beam profile; and wherein the detector furthercomprises at least one evaluation device configured for performing thefollowing steps: at least one image compression step, wherein the imagecompression step comprises compressing the reflection image into acompressed reflection image having a second bit depth lower than thefirst bit depth, wherein the compression comprises applying a non-lineargrey value transformation on the sensor signals; and at least oneevaluation step, wherein the evaluation step comprises evaluating thecompressed reflection image, wherein the evaluation comprisesdetermining at least one longitudinal coordinate for the reflectionfeature by analysis of its respective beam profile.
 39. The detector ofclaim 38, wherein the detector is integrated into a mobile communicationdevice, a tablet computer, or a portable computer.
 40. A non-transientcomputer-readable medium including instructions that, when executed byone or more processors, cause the one or more processors to perform themethod according to claim 1.